{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Fully-Connected Neural Nets\n",
    "In the previous homework you implemented a fully-connected two-layer neural network on CIFAR-10. The implementation was simple but not very modular since the loss and gradient were computed in a single monolithic function. This is manageable for a simple two-layer network, but would become impractical as we move to bigger models. Ideally we want to build networks using a more modular design so that we can implement different layer types in isolation and then snap them together into models with different architectures.\n",
    "\n",
    "In this exercise we will implement fully-connected networks using a more modular approach. For each layer we will implement a `forward` and a `backward` function. The `forward` function will receive inputs, weights, and other parameters and will return both an output and a `cache` object storing data needed for the backward pass, like this:\n",
    "\n",
    "```python\n",
    "def layer_forward(x, w):\n",
    "  \"\"\" Receive inputs x and weights w \"\"\"\n",
    "  # Do some computations ...\n",
    "  z = # ... some intermediate value\n",
    "  # Do some more computations ...\n",
    "  out = # the output\n",
    "   \n",
    "  cache = (x, w, z, out) # Values we need to compute gradients\n",
    "   \n",
    "  return out, cache\n",
    "```\n",
    "\n",
    "The backward pass will receive upstream derivatives and the `cache` object, and will return gradients with respect to the inputs and weights, like this:\n",
    "\n",
    "```python\n",
    "def layer_backward(dout, cache):\n",
    "  \"\"\"\n",
    "  Receive derivative of loss with respect to outputs and cache,\n",
    "  and compute derivative with respect to inputs.\n",
    "  \"\"\"\n",
    "  # Unpack cache values\n",
    "  x, w, z, out = cache\n",
    "  \n",
    "  # Use values in cache to compute derivatives\n",
    "  dx = # Derivative of loss with respect to x\n",
    "  dw = # Derivative of loss with respect to w\n",
    "  \n",
    "  return dx, dw\n",
    "```\n",
    "\n",
    "After implementing a bunch of layers this way, we will be able to easily combine them to build classifiers with different architectures.\n",
    "\n",
    "In addition to implementing fully-connected networks of arbitrary depth, we will also explore different update rules for optimization, and introduce Dropout as a regularizer and Batch Normalization as a tool to more efficiently optimize deep networks.\n",
    "  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# As usual, a bit of setup\n",
    "\n",
    "import time\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from cs231n.classifiers.fc_net import *\n",
    "from cs231n.data_utils import get_CIFAR10_data\n",
    "from cs231n.gradient_check import eval_numerical_gradient, eval_numerical_gradient_array\n",
    "from cs231n.solver import Solver\n",
    "\n",
    "%matplotlib inline\n",
    "plt.rcParams['figure.figsize'] = (10.0, 8.0) # set default size of plots\n",
    "plt.rcParams['image.interpolation'] = 'nearest'\n",
    "plt.rcParams['image.cmap'] = 'gray'\n",
    "\n",
    "# for auto-reloading external modules\n",
    "# see http://stackoverflow.com/questions/1907993/autoreload-of-modules-in-ipython\n",
    "%load_ext autoreload\n",
    "%autoreload 2\n",
    "\n",
    "def rel_error(x, y):\n",
    "  \"\"\" returns relative error \"\"\"\n",
    "  return np.max(np.abs(x - y) / (np.maximum(1e-8, np.abs(x) + np.abs(y))))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "X_val:  (1000, 3, 32, 32)\n",
      "X_train:  (49000, 3, 32, 32)\n",
      "X_test:  (1000, 3, 32, 32)\n",
      "y_val:  (1000,)\n",
      "y_train:  (49000,)\n",
      "y_test:  (1000,)\n"
     ]
    }
   ],
   "source": [
    "# Load the (preprocessed) CIFAR10 data.\n",
    "\n",
    "data = get_CIFAR10_data()\n",
    "for k, v in data.iteritems():\n",
    "  print '%s: ' % k, v.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Affine layer: foward\n",
    "Open the file `cs231n/layers.py` and implement the `affine_forward` function.\n",
    "\n",
    "Once you are done you can test your implementaion by running the following:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Testing affine_forward function:\n",
      "difference:  9.76985004799e-10\n"
     ]
    }
   ],
   "source": [
    "# Test the affine_forward function\n",
    "\n",
    "num_inputs = 2\n",
    "input_shape = (4, 5, 6)\n",
    "output_dim = 3\n",
    "\n",
    "input_size = num_inputs * np.prod(input_shape)\n",
    "weight_size = output_dim * np.prod(input_shape)\n",
    "\n",
    "x = np.linspace(-0.1, 0.5, num=input_size).reshape(num_inputs, *input_shape)\n",
    "w = np.linspace(-0.2, 0.3, num=weight_size).reshape(np.prod(input_shape), output_dim)\n",
    "b = np.linspace(-0.3, 0.1, num=output_dim)\n",
    "\n",
    "out, _ = affine_forward(x, w, b)\n",
    "correct_out = np.array([[ 1.49834967,  1.70660132,  1.91485297],\n",
    "                        [ 3.25553199,  3.5141327,   3.77273342]])\n",
    "\n",
    "# Compare your output with ours. The error should be around 1e-9.\n",
    "print 'Testing affine_forward function:'\n",
    "print 'difference: ', rel_error(out, correct_out)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Affine layer: backward\n",
    "Now implement the `affine_backward` function and test your implementation using numeric gradient checking."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Testing affine_backward function:\n",
      "dx error:  1.64147720918e-09\n",
      "dw error:  1.49766226037e-10\n",
      "db error:  8.98842548094e-12\n"
     ]
    }
   ],
   "source": [
    "# Test the affine_backward function\n",
    "\n",
    "x = np.random.randn(10, 2, 3)\n",
    "w = np.random.randn(6, 5)\n",
    "b = np.random.randn(5)\n",
    "dout = np.random.randn(10, 5)\n",
    "\n",
    "dx_num = eval_numerical_gradient_array(lambda x: affine_forward(x, w, b)[0], x, dout)\n",
    "dw_num = eval_numerical_gradient_array(lambda w: affine_forward(x, w, b)[0], w, dout)\n",
    "db_num = eval_numerical_gradient_array(lambda b: affine_forward(x, w, b)[0], b, dout)\n",
    "\n",
    "_, cache = affine_forward(x, w, b)\n",
    "dx, dw, db = affine_backward(dout, cache)\n",
    "\n",
    "# The error should be around 1e-10\n",
    "print 'Testing affine_backward function:'\n",
    "print 'dx error: ', rel_error(dx_num, dx)\n",
    "print 'dw error: ', rel_error(dw_num, dw)\n",
    "print 'db error: ', rel_error(db_num, db)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# ReLU layer: forward\n",
    "Implement the forward pass for the ReLU activation function in the `relu_forward` function and test your implementation using the following:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(3, 4) (3, 4) (3, 4)\n",
      "Testing relu_forward function:\n",
      "difference:  4.99999979802e-08\n"
     ]
    }
   ],
   "source": [
    "# Test the relu_forward function\n",
    "\n",
    "x = np.linspace(-0.5, 0.5, num=12).reshape(3, 4)\n",
    "\n",
    "out, _ = relu_forward(x)\n",
    "correct_out = np.array([[ 0.,          0.,          0.,          0.,        ],\n",
    "                        [ 0.,          0.,          0.04545455,  0.13636364,],\n",
    "                        [ 0.22727273,  0.31818182,  0.40909091,  0.5,       ]])\n",
    "\n",
    "print x.shape, out.shape, correct_out.shape\n",
    "# Compare your output with ours. The error should be around 1e-8\n",
    "print 'Testing relu_forward function:'\n",
    "print 'difference: ', rel_error(out, correct_out)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# ReLU layer: backward\n",
    "Now implement the backward pass for the ReLU activation function in the `relu_backward` function and test your implementation using numeric gradient checking:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Testing relu_backward function:\n",
      "dx error:  3.27562630807e-12\n"
     ]
    }
   ],
   "source": [
    "x = np.random.randn(10, 10)\n",
    "dout = np.random.randn(*x.shape)\n",
    "\n",
    "dx_num = eval_numerical_gradient_array(lambda x: relu_forward(x)[0], x, dout)\n",
    "\n",
    "_, cache = relu_forward(x)\n",
    "dx = relu_backward(dout, cache)\n",
    "\n",
    "# The error should be around 1e-12\n",
    "print 'Testing relu_backward function:'\n",
    "print 'dx error: ', rel_error(dx_num, dx)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# \"Sandwich\" layers\n",
    "There are some common patterns of layers that are frequently used in neural nets. For example, affine layers are frequently followed by a ReLU nonlinearity. To make these common patterns easy, we define several convenience layers in the file `cs231n/layer_utils.py`.\n",
    "\n",
    "For now take a look at the `affine_relu_forward` and `affine_relu_backward` functions, and run the following to numerically gradient check the backward pass:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Testing affine_relu_forward:\n",
      "dx error:  1.26456291094e-10\n",
      "dw error:  1.25472314781e-08\n",
      "db error:  1.92909759771e-11\n"
     ]
    }
   ],
   "source": [
    "from cs231n.layer_utils import affine_relu_forward, affine_relu_backward\n",
    "\n",
    "x = np.random.randn(2, 3, 4)\n",
    "w = np.random.randn(12, 10)\n",
    "b = np.random.randn(10)\n",
    "dout = np.random.randn(2, 10)\n",
    "\n",
    "out, cache = affine_relu_forward(x, w, b)\n",
    "dx, dw, db = affine_relu_backward(dout, cache)\n",
    "\n",
    "dx_num = eval_numerical_gradient_array(lambda x: affine_relu_forward(x, w, b)[0], x, dout)\n",
    "dw_num = eval_numerical_gradient_array(lambda w: affine_relu_forward(x, w, b)[0], w, dout)\n",
    "db_num = eval_numerical_gradient_array(lambda b: affine_relu_forward(x, w, b)[0], b, dout)\n",
    "\n",
    "print 'Testing affine_relu_forward:'\n",
    "print 'dx error: ', rel_error(dx_num, dx)\n",
    "print 'dw error: ', rel_error(dw_num, dw)\n",
    "print 'db error: ', rel_error(db_num, db)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Loss layers: Softmax and SVM\n",
    "You implemented these loss functions in the last assignment, so we'll give them to you for free here. You should still make sure you understand how they work by looking at the implementations in `cs231n/layers.py`.\n",
    "\n",
    "You can make sure that the implementations are correct by running the following:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Testing svm_loss:\n",
      "loss:  8.99995159931\n",
      "dx error:  3.0387355051e-09\n",
      "\n",
      "Testing softmax_loss:\n",
      "loss:  2.30258066796\n",
      "dx error:  9.06327084654e-09\n"
     ]
    }
   ],
   "source": [
    "num_classes, num_inputs = 10, 50\n",
    "x = 0.001 * np.random.randn(num_inputs, num_classes)\n",
    "y = np.random.randint(num_classes, size=num_inputs)\n",
    "\n",
    "dx_num = eval_numerical_gradient(lambda x: svm_loss(x, y)[0], x, verbose=False)\n",
    "loss, dx = svm_loss(x, y)\n",
    "\n",
    "# Test svm_loss function. Loss should be around 9 and dx error should be 1e-9\n",
    "print 'Testing svm_loss:'\n",
    "print 'loss: ', loss\n",
    "print 'dx error: ', rel_error(dx_num, dx)\n",
    "\n",
    "dx_num = eval_numerical_gradient(lambda x: softmax_loss(x, y)[0], x, verbose=False)\n",
    "loss, dx = softmax_loss(x, y)\n",
    "\n",
    "# Test softmax_loss function. Loss should be 2.3 and dx error should be 1e-8\n",
    "print '\\nTesting softmax_loss:'\n",
    "print 'loss: ', loss\n",
    "print 'dx error: ', rel_error(dx_num, dx)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Two-layer network\n",
    "In the previous assignment you implemented a two-layer neural network in a single monolithic class. Now that you have implemented modular versions of the necessary layers, you will reimplement the two layer network using these modular implementations.\n",
    "\n",
    "Open the file `cs231n/classifiers/fc_net.py` and complete the implementation of the `TwoLayerNet` class. This class will serve as a model for the other networks you will implement in this assignment, so read through it to make sure you understand the API. You can run the cell below to test your implementation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Testing initialization ... \n",
      "Testing test-time forward pass ... \n",
      "Testing training loss (no regularization)\n",
      "Running numeric gradient check with reg =  0.0\n",
      "W1 relative error: 1.22e-08\n",
      "W2 relative error: 3.34e-10\n",
      "b1 relative error: 4.73e-09\n",
      "b2 relative error: 4.33e-10\n",
      "Running numeric gradient check with reg =  0.7\n",
      "W1 relative error: 2.53e-07\n",
      "W2 relative error: 1.37e-07\n",
      "b1 relative error: 1.56e-08\n",
      "b2 relative error: 9.09e-10\n"
     ]
    }
   ],
   "source": [
    "N, D, H, C = 3, 5, 50, 7\n",
    "X = np.random.randn(N, D)\n",
    "y = np.random.randint(C, size=N)\n",
    "\n",
    "std = 1e-2\n",
    "model = TwoLayerNet(input_dim=D, hidden_dim=H, num_classes=C, weight_scale=std)\n",
    "\n",
    "print 'Testing initialization ... '\n",
    "W1_std = abs(model.params['W1'].std() - std)\n",
    "b1 = model.params['b1']\n",
    "W2_std = abs(model.params['W2'].std() - std)\n",
    "b2 = model.params['b2']\n",
    "assert W1_std < std / 10, 'First layer weights do not seem right'\n",
    "assert np.all(b1 == 0), 'First layer biases do not seem right'\n",
    "assert W2_std < std / 10, 'Second layer weights do not seem right'\n",
    "assert np.all(b2 == 0), 'Second layer biases do not seem right'\n",
    "\n",
    "print 'Testing test-time forward pass ... '\n",
    "model.params['W1'] = np.linspace(-0.7, 0.3, num=D*H).reshape(D, H)\n",
    "model.params['b1'] = np.linspace(-0.1, 0.9, num=H)\n",
    "model.params['W2'] = np.linspace(-0.3, 0.4, num=H*C).reshape(H, C)\n",
    "model.params['b2'] = np.linspace(-0.9, 0.1, num=C)\n",
    "X = np.linspace(-5.5, 4.5, num=N*D).reshape(D, N).T\n",
    "scores = model.loss(X)\n",
    "correct_scores = np.asarray(\n",
    "  [[11.53165108,  12.2917344,   13.05181771,  13.81190102,  14.57198434, 15.33206765,  16.09215096],\n",
    "   [12.05769098,  12.74614105,  13.43459113,  14.1230412,   14.81149128, 15.49994135,  16.18839143],\n",
    "   [12.58373087,  13.20054771,  13.81736455,  14.43418138,  15.05099822, 15.66781506,  16.2846319 ]])\n",
    "scores_diff = np.abs(scores - correct_scores).sum()\n",
    "assert scores_diff < 1e-6, 'Problem with test-time forward pass'\n",
    "\n",
    "print 'Testing training loss (no regularization)'\n",
    "y = np.asarray([0, 5, 1])\n",
    "loss, grads = model.loss(X, y)\n",
    "correct_loss = 3.4702243556\n",
    "assert abs(loss - correct_loss) < 1e-10, 'Problem with training-time loss'\n",
    "\n",
    "model.reg = 1.0\n",
    "loss, grads = model.loss(X, y)\n",
    "correct_loss = 26.5948426952\n",
    "assert abs(loss - correct_loss) < 1e-10, 'Problem with regularization loss'\n",
    "\n",
    "for reg in [0.0, 0.7]:\n",
    "  print 'Running numeric gradient check with reg = ', reg\n",
    "  model.reg = reg\n",
    "  loss, grads = model.loss(X, y)\n",
    "\n",
    "  for name in sorted(grads):\n",
    "    f = lambda _: model.loss(X, y)[0]\n",
    "    grad_num = eval_numerical_gradient(f, model.params[name], verbose=False)\n",
    "    print '%s relative error: %.2e' % (name, rel_error(grad_num, grads[name]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Solver\n",
    "In the previous assignment, the logic for training models was coupled to the models themselves. Following a more modular design, for this assignment we have split the logic for training models into a separate class.\n",
    "\n",
    "Open the file `cs231n/solver.py` and read through it to familiarize yourself with the API. After doing so, use a `Solver` instance to train a `TwoLayerNet` that achieves at least `50%` accuracy on the validation set."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(Iteration 1 / 4900) loss: 2.299060\n",
      "(Epoch 0 / 10) train acc: 0.123000; val_acc: 0.123000\n",
      "(Iteration 101 / 4900) loss: 1.900392\n",
      "(Iteration 201 / 4900) loss: 1.703651\n",
      "(Iteration 301 / 4900) loss: 1.626836\n",
      "(Iteration 401 / 4900) loss: 1.619391\n",
      "(Epoch 1 / 10) train acc: 0.452000; val_acc: 0.456000\n",
      "(Iteration 501 / 4900) loss: 1.552999\n",
      "(Iteration 601 / 4900) loss: 1.595678\n",
      "(Iteration 701 / 4900) loss: 1.540174\n",
      "(Iteration 801 / 4900) loss: 1.557086\n",
      "(Iteration 901 / 4900) loss: 1.277470\n",
      "(Epoch 2 / 10) train acc: 0.494000; val_acc: 0.460000\n",
      "(Iteration 1001 / 4900) loss: 1.364557\n",
      "(Iteration 1101 / 4900) loss: 1.276141\n",
      "(Iteration 1201 / 4900) loss: 1.470227\n",
      "(Iteration 1301 / 4900) loss: 1.513791\n",
      "(Iteration 1401 / 4900) loss: 1.151698\n",
      "(Epoch 3 / 10) train acc: 0.488000; val_acc: 0.477000\n",
      "(Iteration 1501 / 4900) loss: 1.509078\n",
      "(Iteration 1601 / 4900) loss: 1.360453\n",
      "(Iteration 1701 / 4900) loss: 1.654545\n",
      "(Iteration 1801 / 4900) loss: 1.208698\n",
      "(Iteration 1901 / 4900) loss: 1.297615\n",
      "(Epoch 4 / 10) train acc: 0.516000; val_acc: 0.491000\n",
      "(Iteration 2001 / 4900) loss: 1.522669\n",
      "(Iteration 2101 / 4900) loss: 1.155986\n",
      "(Iteration 2201 / 4900) loss: 1.386201\n",
      "(Iteration 2301 / 4900) loss: 1.266431\n",
      "(Iteration 2401 / 4900) loss: 1.275576\n",
      "(Epoch 5 / 10) train acc: 0.560000; val_acc: 0.503000\n",
      "(Iteration 2501 / 4900) loss: 1.176829\n",
      "(Iteration 2601 / 4900) loss: 1.411111\n",
      "(Iteration 2701 / 4900) loss: 1.218289\n",
      "(Iteration 2801 / 4900) loss: 1.010762\n",
      "(Iteration 2901 / 4900) loss: 1.167803\n",
      "(Epoch 6 / 10) train acc: 0.586000; val_acc: 0.518000\n",
      "(Iteration 3001 / 4900) loss: 1.143777\n",
      "(Iteration 3101 / 4900) loss: 1.118263\n",
      "(Iteration 3201 / 4900) loss: 1.299282\n",
      "(Iteration 3301 / 4900) loss: 1.206645\n",
      "(Iteration 3401 / 4900) loss: 1.131666\n",
      "(Epoch 7 / 10) train acc: 0.593000; val_acc: 0.518000\n",
      "(Iteration 3501 / 4900) loss: 1.225119\n",
      "(Iteration 3601 / 4900) loss: 1.380260\n",
      "(Iteration 3701 / 4900) loss: 1.000274\n",
      "(Iteration 3801 / 4900) loss: 1.075434\n",
      "(Iteration 3901 / 4900) loss: 1.141873\n",
      "(Epoch 8 / 10) train acc: 0.565000; val_acc: 0.513000\n",
      "(Iteration 4001 / 4900) loss: 1.097573\n",
      "(Iteration 4101 / 4900) loss: 1.156408\n",
      "(Iteration 4201 / 4900) loss: 1.392913\n",
      "(Iteration 4301 / 4900) loss: 1.042721\n",
      "(Iteration 4401 / 4900) loss: 1.138546\n",
      "(Epoch 9 / 10) train acc: 0.615000; val_acc: 0.504000\n",
      "(Iteration 4501 / 4900) loss: 1.138931\n",
      "(Iteration 4601 / 4900) loss: 1.213082\n",
      "(Iteration 4701 / 4900) loss: 1.320596\n",
      "(Iteration 4801 / 4900) loss: 0.990728\n",
      "(Epoch 10 / 10) train acc: 0.624000; val_acc: 0.530000\n"
     ]
    }
   ],
   "source": [
    "model = TwoLayerNet()\n",
    "solver = None\n",
    "\n",
    "##############################################################################\n",
    "# TODO: Use a Solver instance to train a TwoLayerNet that achieves at least  #\n",
    "# 50% accuracy on the validation set.                                        #\n",
    "##############################################################################\n",
    "pass\n",
    "solver = Solver(model, data,\n",
    "                    update_rule='sgd',\n",
    "                    optim_config={\n",
    "                    'learning_rate': 1e-3,\n",
    "                    },\n",
    "                    lr_decay=0.95,\n",
    "                    num_epochs=10, batch_size=100,\n",
    "                    print_every=100)\n",
    "solver.train()\n",
    "##############################################################################\n",
    "#                             END OF YOUR CODE                               #\n",
    "##############################################################################"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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HlMyWSwC87ZZrgBlepu6I3H///Zx1VgMfffQI+/fXAHcDJcAoYCBwIjAPCGAtVFdfzeTJ\nbh4dcKieLj8//4jn0wWDlUyePJ/q6pEA5OY+xaJF08nPH3Skb0tERERERCROSUkJJSUlx+z8SQ0W\nN8akAS8CL1lr5yV4/jHgD9baVd7PW4AR1tr9McdZ/3qhUIgHHpjNrFnvAXcCC4HuQG9gXNT5s7OL\nWbAgxJw56yKCsJIjCsJCoRAFBTPZtGkuGmIuIiIiIiKtKdWDxZMN6JYAn1hrb2/i+UuAW6y1lxpj\nvg7MtdbGNUWJDOjABVe9e1/J7t0GuAr4I67T5bVRr8vKepoePV6hunoBRxuElZeXU1i4kwMHxkQ9\nnp1dTGlpbwoKCpI+l4iIiIiIyOFIdUCXzNiCc3ER1nnGmKAxZqMx5iJjzHRjzDQAa+1vgQ+MMduB\n+cB/JXPxYLCS/ftDwGogF9fx8k1cUxRfiB49itmz52JiRxpUV4/QSAMRERERETlutVhDZ639I9Al\nieNuPZwLNzQ0MGbMj6mru85bhp8Zy8aYG8jIuIxAoAs5OSXccccYpk1LTRCbn59Pbu5TbNo0ishs\nX27u6+Tnj07JNURERERERFpDmxSMBYOVDBo0kV27vo7rcBlpEOnpl7BwYR2lpb3ZuHEe11wzltzc\nEmIzdy4IO7yRBoFAgEWLpjN48Eyys4vJzi4mL28GixZNV/2ciIiIiIh0KEnV0KXsYsbYxsZGrynJ\nJGAnUApENyjJzZ1GVdWCqAAr3JlyBAA5OSU8+eTNR9yZMhQKpaRjpoiIiIiISLLapClKyi5mjC0r\nK/OakowCZgDTcB0uRwCWzMzlrF9/PwUFZ8S9PlEQpsBMREREREQ6ilZvinLsVAF7gB/hOltWk5b2\nSx5//PqEwRy47ZIFBQUUFBQQCAQIBispKJhJYeFOCgt3UlAwk2CwshXfg4iIiIiISNtpky2XQ4bM\noKLC4urnHgXKgZcB6NfvXTZvXsqaNWsAGD9+PGlp8b1bNE9OREREREQ6mg6foQsEAtxxx3Dgy8A3\ngN8C9wDbgQy2b88mK2sskyalM2lSOiecMI4VK56PO08wGPSGjGuUgYiIiIiIHJ/aJI01YEBfMjO7\nAg3Ak0A34CTgNODvwPPAOGAsNTV3ceONj1JXV9cWSxUREREREWm32iSgy8/Pp3//3bhmKEO9R+cB\nO3AzzANAJTAT2E1t7X+SkzM5qj7OzZMrIRWjDERERERERDqiNgnoAoEATz55M1/6Ug3wEXCZ98xa\n788QMB83zmAMMI5du5YwefJ8QqHQoXNonpyIiIiIiBzPWr0pSuT16urq+Ld/u5CGhpuBXOB9YCVw\nF7ALuDLq9dnZxZSU9DwUsPmZOI0tEBERERGRjqDDz6GLvd7y5c8xceJC4F5gN5AG/By4DZgQcWSI\ntLQ7+Pznd/P3v19FIBAgN/d1Fi2afsTDxUVERERERFpTpwvoADZsqGDMmNns2dMAFOFq5wyurs6v\np3sQ+AfwrPeqIBAiL28JGzfOU2ZORERERETavQ4/tiCRtLQ0TjnlFLp06QecD/QFPgVuAFbgMnZ1\nwHW4geQzgZ3Abt5551NWrChuk3VL2wuFQpSXl1NeXn6ovlJERERE5HjR5hm68IDwXwC3A+OBx4DF\nuOYoPwe6Al2APkAprllKeJh4bu40qqoWKEt3nAkGK5k8eb43jxByc0u0BVdERERE2rVOt+WyvLyc\nwsKdHDjQC1gPvAF8G8jDBXYB3Jy6XGANcBXxzVLWUFrah4KCgmP+HqR9CP9DQHRwP3jwTMrL5yq4\nFxEREZF2qVNuuXRb5bYBZbgsXQiY7T17LlABvA6Mxm29jJWy+yEdRDAY9DJzkb/CAaqrRxzqeioi\nIiIi0tm1eUCXl5cHLAeeAS7xHl3u/TkNl7U7G/gAeB74LYmGiefl5amWSkREREREjittHtBVVFRQ\nX38ybotlV1ym7mRccLcQmArswDVIOQPYxJe+NJ7MzNVkZ68hL28Gd955AV/96ncZNmwtw4atZciQ\nGQSDlUe8JjXaaP/y8/PJzS0hUXDvzycUEREREens0tp6AaFQiMbGfcBFwKvATbjRBe8Dhbigzq+T\nqgT2sm/f18nI2MHnP7+W22+/kVmz1rBt24m47phQUbGL8eNnU1m5iIqKCqD5oeOhUChim146U6Ys\njGi08VSbNtqIXJsGp4cFAgEWLZrO5Mkzqa4eAUBOTgmLFt2seyQiIiIix402b4qyYcMGzjqrGngT\nN6rgBNyA8b8AV+CCtCuBBuBmYAFudMF8YATwHlANPEVkcwy4kpycU9i7123jbKoDYmSnRGtDQBE1\nNWtoD4021MWxZQp4RURERKQj6ZRdLocN+4CDB0NAI/AfuMYoF+Dq6mYCZwJ3AtfixhrMJJy1Wwak\nA+O8M4aAcmAOboad/wG/gdzciSxb9j0KCgoIBAIJOiWWAx8S30WzmNLS3q3aRVNdHEVEREREOp9O\n1+UyPz+fAQNKcTPm0nBbLH8G9MaNK/gj8Cvgc0AGEARGEl56f1wgCG5L5kygBBeUBSIev53q6jEU\nFn5IQcFMgsHKJjolpq5j5tHU4qmLo4iIiIiItKTNAzq/FiovbwlumPgIoAB4ApeRKwS+jJs/9zrR\nTTDwjv0DbkvmfFzm7jyit1/6j4/j4MGr2LRpLpMnz08QZOXjgsEjb7ThB3HLlz/NkCEzKCzcSWHh\nzkNBpIiIiIiISKq0eUAHkJ8/iCeemEa3bnVALW5ZI3GZt7647pcBYDqwBHiOcNAVAG4jPf1CYJj3\ncx6usUqI+Iyee43fSCO6U2IAmEpW1liyslaTmfkQOTnX8PjjUw9t0Wwu4xYMVlJQMJPhwz9g0qQX\nqKiYx4EDYzhwYEwzQWRT90RdHEVEREREpHntIqALhUJMmbKQ/ftfAv6EC2LOB17CBWe7cFm4gcA8\n4HJgGsYUkZW1mpycB7j77gvJzPS3Sz4DnIPbfvkaLnsXz88ODh48k+zsYrKzi8nLW8DChdfRo8er\nQB/27h3HlCkLWbHieQoKZjaZcQuFQkyePJ9Nm+ZSU9MHa0dxNNslE69tBosWTVf9nIiIiIiIAO2g\nKQq4xiiFhTs5cGAMrt5tPnAqLiDaDPwTsLgaulFAiH791nLhhSfxm9/s55NPRuPOW0RNzWpgIm6L\n5mhco5P5uO6YiZuLRHZKzMvLY+jQ22OakTSQlTWu2e6X0e+hHNgJjIl6n0fSXEVdHEVEREREOo9U\nN0Vp8zl08Qbh6t2W4ebS/TtwBy4gGwZ8SCDwHDCIxx77X6xdQjjIyiEt7TwaGv4LV283GhgKZAMz\nMOZssrLSycl5PWpeWSAQOBRklZeXJ2hGUkFNzXiayrjFB2j5uDEKkVk6f7vk6MO6G5FrExERERER\nidQu0j3x9WIBXDbuFODruM6XjwKnAV8mFDqd7dunx2xrrASeoKHhIu/n6bgtl8XAFuBv3H//bkpL\n+7Bx47yUz3KLfg9+vd8MjCkiO3uNtkuKiIiIiEjKtYstlxA5RHsE1oYIhYqorU3HdbFMBzZ6R56O\nC+z6Et7WGCI8m+55XIfMX/tnBkLk5S1h48Z5LQZUiee/tbzlMvY9APTr9wfuvLOQAQP6dqjtktrm\nKSIiIiJybLT6YHFjzBPAZcB+a+2ZCZ4/Bbc/8stAF+ARa+3iJs7VZEAH4UCisrKSKVP+Sn29P+i7\nF27r5TxcgLYTt53RD+L840bjBozfh8vqjQAsxizmrbd+ytCheS1e20lnypSFhwKznJwSfvCDC3no\nod9HPfbkkzfHZfo6ejAUDkpHAq4L6KJF01Oe0RQREREROR61RUA3DPgMWNJEQDcLyLTW3mWM+Tyw\nFehmrY1rLZlMQLdiRTF33bWM3btPxNXO3QxcgutwGZuNqwJmA7uBqbhumKfhgjp/ZAFANUuXNjJx\n4sSE1y0v38yECfPYtetbBAIBcnNf5/HHpwJ1QDgwS2WwFnsuoM0DwcTZyfhMpIiIiIiIHJlWb4pi\nrV1vjOnVzCH7gDO87/8P8JdEwVxLgsFKbrzxMd5551Os/SGwHZdluwzX4dLn16fNBL4GHMQ1UnkD\nF0ueFnGc30xkO26mXbzy8s0MHz4rajvlpk2jmTIlPohJVYMSPwu2dWsh1n7AF74wi6ysL7Nnz8UA\n5OY+1SZZsWAwmKAhTHPNX0REREREpC2lIuWyEBhkjPkzUAHMONwT+DPcKiqu8xqd7MRtoSwE9gIb\ncHPo/KYpg4CbSEubDwwBzsMFcucBS4kdxp2evoLx48cfupY/HLyhoYEJE35KTc21xAcxhUnPjIs9\nb3PDw8Pz6qZSU1PKwYN92L37ZKqr5yc9hDzZa4mIiIiISOeWirEFdwEV1tpvGGP6Ar8zxpxprf0s\n0cH33nvvoe9HjhzJyJEjYzJDIWAd8DHQFRekpQH/A1yPy9jV0aXL0zQ0PAKUeK95C9cZ8yZgLHC1\nd5Ul/OhH55OWlha3tbJ794f58MN8ID7j2VIwtXXrVvr3709BQQEVFVUxdWcuw5aXNzBuG2UwGGTr\n1kJcHDwXty3U79bpbxMNUVV1KkVFRUyYMCEqSxhf43b02Tx/+2coFCIn5w9UVDQ/bqE91wm257WJ\niIiIyPGnpKSEkpKSY3b+pLpcelsuX2iihu63wE+ttX/0fn4N+IG1tizBsS0MFh8FXIkbDJ4LfAe4\n1XusASgCPgDWAtOAa3AJwY9xmbqewJnAr3D9WQA+YcmSKzj99NPjtla6zF81LhiMrhvLzZ1GVdWC\nqNq5LVt2cN99L7B9O1h7GcY00q/fWowxVFc/FvP6G8jKOpFt274BhJuLhEIHGTZsLQcP9sXVBPpD\nyPvjBqD3A8qAi0hPT+P009cfCtiORY1bbIDYvftzQCZ79nwLiG/+0p6bprTntYmIiIiIQOpr6LDW\ntvgF9AY2N/HcI8As7/tuuA4lJzdxrE2ksbHRDh58m4V6C5dYKLJgLWyy8G3v8TILqy38l4VCC2u8\nY/7bwlctPGBhhYXbLDR6z1kLjTYnZ7LNyRkX8RrrHfO2NWacd52bLKy0sMpmZo6yZWXvWGut3bjx\nXTt48G02K+tpCxMt3Bpz/retMUURP7tzGzMpbh2DB99m6+vrbW7u1RFrafTOeav3PuPXP3jwbbax\nsdGWlZXZ7OzimGtZm529xpaVlSW8t80J3/fo6+Xl3WrffvttW1ZWZhsbG1s83l/fkfDfV+y1UvVe\njmZtIiIiIiKp5sVEScVhyXy1mNIxxhThOo7kGmN2GWNuNMZMN8ZM8w75OfBVY0wF8DvgTmvtXw8n\nqAwEAixaNJ3c3InAJO9yISAPuAG3hXIb8AKuEcp/Aq/julC+BvwJ+DPwGG5Ugb99sRwIsmtXDjt3\nDiG8tXIzLsO3A2v/DfguUIvL/m3lpJPq2LKlioaGBq/ebS41NX1w2b9vEFtvZ20XogWx9rK4dWzd\nOpyKigqKiu4mK2s54SHkw4FzcCWII+PO7zclSbWmmqBs2zbyUAOY6O2ezTdNOfzrV1JQMJPCwp0U\nFu6koGAmwWBlSt/Lsbp3IiIiIiLtQYsBnbV2grX2K9baDGttT2vtk9ba+dbaBd7zn1hrL7fW5llr\nz7TWrjiSheTnD2LJku+Snh7AjSqYATwIrASeAXII15r5nS4vA8bjauwKcQFgHVCJ64K5E9hJbe06\nrO2Fq7erAGZ5518P1OOarDwFXA78L/v2TWbSpEZOP/1atmwpbOE25WPMi0Q3YmnAmIa4dRw8+AJb\ntuygoOAM1q27j9zcaWRmriYz8wNMEknX/Px8cnNLiG364mrc8ls+QYtc8BkKvX/Mm62Em8PMTboZ\nzPFGzW9EREREpCXtpmNEMFjJ9OnLqK+PDI4Mrs4scpnjcUHeQODaiMfrgbOBP+IydXNxAWAv4B66\ndFmNa5gyE1d7txC4jnDWDVwN21zgSqy9hm3bvk9tbb33XD5uzl1kt02nXz/Iy5tBRsZK0tK+R9eu\nD2DtSxHrGAOMwdrFPPxwqVcLdwZVVQtYv74vpaXnceaZb+AC0hKaCtj8TObgwTPJzi4mO7uYvLwZ\nLFo0PWH9XEsBQXSA6AefH1Jb24OpU5fEZctSGVCmOqN27IPd1pXK7KWIiIiIdF5JNUVJ2cWaaIoS\n3eyjEpf9IfwQAAAgAElEQVRBW4Pr+LiT6IHiU4F7cLPOv+j9WYxrmPJ74FLv2DNxAdpI7ypLOPHE\nT/j004uBdKAvLthbG/G9f61DK8OYG7B2MS7wqMQNMre47GA9PXqs4bLL+vLii7vYvTvgnfspb/2N\nuOAxLDu7mNLS3nEz3fyGHlVVfamrKwcuIiOjK/37l0Y1JfHvV0udHJNtEBI9/89/n+69J2q2Ej7v\nCKwN0b37GmbNupJrrhmbVFMWf+1VVVVMn57FgQNXJnV/khG5Nohv6NJRaMC7iIiISOeV6qYo7SKg\nC3e59Ls+fojrbOkHcf4H2824YG81ru5tHq6j5cu4wO1l4GRgNK4Oz39dpXfc+8BkXAB4FfBt3NbN\nbFy2bjfRAR1kZMyjV6/Nh4Z+9+27lrFjT+Wjj/bzm9/8md27M3EB3r/HnGM1hxPQQfT4AHC1hUfS\nev9wA4INGzZQWPghBw9eldRaQ6EQK1YUc//9r3j3xSTVUTIyyLQ2BBTFdB09+qClM4wtiP7vISw7\nu5iSkp6H3lNHfX8iIiIix7NUB3SpmEN3DPjvz6+VmwmcS3r6B9TXX4W1FbjmJwBbgWeB24Ff4LJy\ni7zXBXDjDh71fv4QN+PuLuBO3PbGPNw2ynnedaNnsA0YsJ3586eybds2rO3KI48EePDBXGpqqoAv\nAcOA7bgtoOHXueuAm4cXfjwnp4T8/HkJ37XfiORotbSdMfYagUCAQCC2sUvz5sxZR3X1gkPX2LRp\nFJMnNx2MRdbMhZvFNJKePoq0tOsAQ05OCYsW3XxUQUqq7mH74uYT1te/ybXXvsTevZcAqZlBKCIi\nIiIdW7v45/1w/VMD7sPrbwnXQg0CfkFu7rPcfbef3QvhauaCuMyc3x3yDOCX3p+1uMzcROCbuA6W\nDbjA7nFcRm0ebtZdMXAbJ5xQRXr6GGAVsIquXS/i73//B+edt5dp0zKZPn0VFRWPUlPzPjAAF1Q+\nixuADq7OrgSXZfwGrrnLTO/8xRhzPXfcUdjusirR9Wd+V84NXvAZX392JPVv0a/x6/W6Ulc3gc9/\nfhkLFjSyceO84zY4iax3zMvLS1DbuIP6+n1s27ZATWRERERE5JB2EVkEAgHuvPMCsrLG4bZFnghc\nSnr6KrKziznzzJn86EejWLZsO9b6Iw1eIrY5SVgdrjbuMeB7uMzbOtw2zIG4rZFXEn77AWAo//zn\n6dTVrcEN9z6N+vr+7Nz5JAcOjKGmpg81NSNwWz1LcUHcB7iRCrtxWT5wAeMjuOBxEG7bZ2+gN5mZ\nV9CvX0+WLVvGsmXLaGhoOLTiVHQ0bGhoYNmyZVRWVpKTE9u8pekGIeGxETdgzA3A+xiznZqaWioq\nqo5oLU0LEW4+MwYYz65dzzBnzvoUX6fjiG2AMnTo7dx55wXk5c3AmNm4e3UabiuxxjKIiIiISFi7\nCOhCoRAPPfR7amruwwVd5wKTMeYx7rprJwBTpvyJbdsuxGW9HsVlw5YAzxHdHTKIy5x9hJvtlg78\nDy6D52fM1uICrkj+7Lg0oAB3ayJnzvmZq+uAG3FBXBkuWLwZ+BQ3M+9d4Ft06fIk4TlzBUAeJ5zw\nKwoLf86kSelMmpTOCSeMY8WK51PS0XDFiuc54YRxTJqUzvXXZ1JVVU2vXjcm1Q0TIC9vIFlZJ3qN\nUcZh7TVUVz+WMAN0JB0lw68ppzVn7bV3TY1veOih3zN//kQyMi6nnfxnKiIiIiLtULtpijJ8+AfU\n1JQSbmQC0EBa2iU0NPQHeuI6UY7DBWM34zJwzwKvAP+BCxYGAF28rzRco5Q03IiDCbjtmbOBf+G2\nQlYcuparhbvW+/7nQH/vegBvATuAXFw3zP64OXn/8NaAd/3XSE9/A2POP9StMi3tY4wpoa7OeseG\ng8SMjDH07HkK27YtjHo8sjlIS40+GhoaOOGEcXENRjIzr2Tt2jt5//336d+/f9yg8EiH2xjlSDpK\nBoOVjB//E6qrx0Tc1+av09k11wBl/vwapk/P9p6LbRAE6nwpIiIi0vGkuilKu/kUaO0HxGdugjQ0\nfAlX6/Y93CBwP+v1LeBmjKknK+ub9Ov3BvfdN5AnnzyVHj3exAWAvwa6eX/+CRe83YfbNnkGLqh4\n3/v6OW6+3XPe4zmAPxOvEpcVDBCukwsBJ3nHjQWW07Xr70lPf4O6uueorZ2BtYuxti/GrKWubgwu\nWIzM+BVTW/svtm37FrEZq61bh1NUVMTSpasYMGAaw4btoLDww4TZu5UrV1JTMz7uHAcPDmf8+P9h\n+vRsRo7c3WTmLxisZOLERzh4MPngPj9/EOXlcykt7U1pae+k6t/y8wexefMSevZ8ns4yL+5Y6t+/\nf0Qm1G8QNANjisjOXtNi1lVEREREOr920eUyPz+fnj0fprr6tJhntuLmvfkfWKfjtjVa4HJgBN26\nLWT06CG88UY3Zs8eAMDJJwP8NzAYGI57m9NwDVBu9c61Czcrzj93H9wcu8eB573Hz/Be83dgGa6T\n5lW4WXizIl6/GZhHfX0fXBOWyNq8NOrqJuLq+nyVuOzip8DPcNs3iXr+4MEXuPHGwTQ0vE5kVm/T\nptHNdpMMc9tPd+1aEvHa+E6U/pa/6mr//Y0lMuh0gdbohFc43I6SflZv//6zvFq98Ky9o+1umUqt\nOfrAbUV9ik2borur5ua+TkHBXBYtymby5JmHMqH9+lnuvDONAQP6kJ8/pt3cMxERERFpG+1iyyVA\neflmhg+fFbNt8C1c9myC93MImIHL2FXhmmsMx2Xg/MDFP2YY8Efg697ry4EVwBBcVm070TPiNgBP\n4rKEkdsBV+G2YF6LC8TmA91xjU7GEb0VLnIQum+19/px3pc/Q8+fWTeK6K10/vofxQWHV+EauIRl\nZ6+htLTPoWAqvOVyNdFbSCPvnf/a6K2N0Vv+/Pc3AmggJ+dVVq26PWHm7XCDnvjZeK4mMTf3ESor\nl5GW1i7+bSHpgezH5pqJt6+mKsDsDDP6RERERDq6TrvlsqDgDNatu4/c3GlkZq4mO3sNZ565lF69\nXiG8Pc8fUwDhTokh4FLCbyWIa2ZyJeHukyFgG65RympcLVxkYFmJCwjfiFlVCHgm4txuhIIbidDo\nPV+ECyr97Zj+9UK4IPEV3HbPAC67OBw4m+gsnj9rrxiYjTHn4AKzAsIz+SJWFdOkJC0tjR/96HyM\nGeW9z23A9+na9fBmy0V25czM3Mny5Ylr4o6kiUv8qAPXWXTPnquoqKho+oWtqKkGJcd6NEBL21f9\nTGhzNZAtSUXjHRERERFpf9pNQAcuqKuqWsD69X0pLe1DMPhLnn32TvLyZpCRsZIuXR4FDhIO7Pyt\ni11jzmRxgdhVuEDpO7iAbSCu8+VKXJDlB17zcVm/CcByogPIfMIjEipx2xIH4DJ3M3BBoh8cVuG2\nUV4JXA+8hl/r59ZRi5t/l0F08OcHUj3p2fMPZGWle+frQ7hezxeiZ89Xo+rNQqEQCxa8jbXPA+O9\nrz/ggs3I1zbQvfvThEKhQwFKfMdKF5gOGLAn4XbK+KBnFJs2TWL8+J9EjWE4GqkY4XAk1yovLz/s\n+XqpkoqgrSmtFai25t+biIiIiDjtKqCD+A+2+fmDeOKJafTs+TsaGw3wJi74aATuwQVLkYFLOvAU\nLtuVgQuWrscFc38G9uHGIuzBzfWajdueCS6jF8DVka0CXvWu81Vc8ObPBLsS+CLRzVoacIHhItyW\nzKeAC3HdNv2ArdZby+u44NAfdbAKWEFOznyeeeYRcnNfx41iKMXV6/nZuzVkZl7JsmW3EQwGD31w\n3rBhAzt3xjZWSaO+fhg5OVPJzi4mI2MemZnj2LVrbFSDFH8G3eDBM5MacZB4QPhuqqvHMGjQzU1m\nfZIddRAMVjJkyAyGDdvBsGE7GDJkxjHLJMVmrSZOfKTNApFjGQwdySD4w7+GMoDtiYJrERGR40e7\nqaFrSrj2ahIu4PLHBXwAXIwL4DJwNXHDcQ1N/orLtr2FC6TKgV8Cn+DGFfTCdah8FHjY+7k/4XEE\n/+OdL+Sdvy+unm0XLutXDnxIuLatEvgJrnauL+E6us245il+bVsVkIUbfeA3VQGXCQyRl7eEjRvn\nUVFRxeTJ86mq6ktdXTnWfpP09L306rWRWbOu4eGHX2Pr1kIA+vcv5eyz4bHHhhNd+xcCZjN9+k5u\nvPFGrrtuIdXVCzjS0Qi+8HiDKzncNvrJ1IoNHHgz1dWPEd0g5Gaqqh5LaeYqvqYPoIGsrPjxD8d6\nNMCxrttrbjRCKkZFJL6XGqnQVtqiDlRERESSl+oaunYf0IU/jPbCBUqjcFsoN+E6Vj6HC7JG4zJ1\ne3EfKk/DbY2cD/wTOA+3DXOI9/xAXHOUe3AZNb9JSRqwGBccBnGdLv+I61J5A+HGJo1EN1VZ5T3m\nB4Z+s5PzcNs9x+OCrGXAJO97//WuI2Vm5muUlIwgLS0t7l/VA4EAeXl5DBr0X3EBT7du57F/f09v\n3X7m7DHga6Sl7eErX1nLxx9P4+DBsVHnPNwP9OXlm7nmmrls316Ptbd59ys6SMjKepoFC2oZOHBg\nwsCwucBxw4YNfO1r27H2mqjXGFPEW2/lMHTo0KTWmdx7SRzkZGTMo1evzezZczGQ3Hy9o9EawdCx\nvsaxDhhbS2doGqPgWkREpP1LdUDXPloLJiUft42xBy6j1gP4LXAXbrbcaFyQ9hGu9ux177E5wDdx\n2y3PIbzlL4QL5ibiAr/HgL8BBwh3olzsHTsSqCAtbRENDWOA3wGZwNXe8xuAhbjs27MR6ywE1hI9\nHmEgLhCdiT+LzjVOuZiDB7tSWPhzAoGJBAKBuH9Z37BhA9u2fYNwl0j34XP//htISyuioWGGd80X\ngDuAhTQ0jGTXrgBuu2c0v5auvLzc3eFmPsRGdyGtIpyRjOTGLUydejmBwE5yc5+Kyww0N+pg69at\nWJse97i1hpdfftnbgnusPmi7+2lMLUuWTD3UdTM/f94x/RDc0nbIVARD/rbayPEHOTklRz0qwg+A\nqqr8zHPHFZ/Viv/d7Qha4/dJRERE2pd2/8+14dorcN0gH8HVq/X1HnscF8RMAN4F3sYFc37t2fdx\ndXZB3KiBPbjM0mpcbZzB1bjNwzUsGY/L5J2DC1xm4hqolNLQcD5wBa6L5s24jN2VwL3Av+Fmyt3u\nre1e3LbNkUTf5q3e+/g1LtP4ArAAF3x+QF3dMxw8ONZrNnIto0Z9l7feeotQKOQFPF0I167t9L5e\nY9SoHmRm7vHe+7dwAeZcXKawB25kg98EphzYQGPjQqZOXdJi3VMoFGLChJ9SU+MPRh+EyzS+SnSA\n/BjWLubgwasSNt5oqa6nf//+GPNixDnBbVtdyc9+NiDhGo+0Vii6ps+/nx9SW9uD6dOXEQhkHpMG\nJW3lSAbBN6e8fDMDB7qB99OmZWBtZDMh6EjD4tuqu6l0fKpVFBGRdsFa22pf7nKHb+PGd+3gwbfZ\n7Ow1NiOjyMIlFm618I7352wL37VwkU1L+4WFURYmWiiyMNbCKu/Y0d6fkywUWFhh4TYLjRashae8\nx662sNp7rt67hn/MYu98jRZu8Z67xXvMeo+XWXjTwgUW1niP+8/5555q4W0Lxd5zZRHfv+utcZKF\nVdaYIpubO9UuWbLCe1+R63Hnzcm5ydbW1tp7773XwgPeud71jr3Ewtyoc8IyC5fHnWfw4NtsY2Nj\n1P0vKyuzmZmzI9ZnD63TmEk2M3OVzcycbY0pinne2uzsNbasrCzi77DYZmcX28GDb7MbN74bdZ3G\nxkabmzvJW/Ma7z6NbnKNyZyzpd+rvLxbrTGTkroPx0Jjo7tWW13/SJSVvWOzsmL/Xtxj2dlP2+zs\nNTYv79bD+rtoS2VlZTY7O/Z3O/y725F0xN+njupo//dHRESOX15MlLIYq93X0Pkit3dNmfIXamvL\nvWcuxmXC/gS8SHg74gZ69vwxRUWzOO+8h6mrewaXiZkHXIBr6/8v4Ae4bFZf3NbJ3bgtkcW42rxG\nwvVylcD9uCYst+FGCvTB1elNJbopCcAcunZdR319MeGmKBkR5+5NuIFKOeHauxne6+cRWQdz5pnf\n4W9/+4DduycSXb8XHjYeCoU466x7cdnDdbh6PX+dr0ecM7KxS3j7ZlbWDtatOy1qa9by5auZOLGe\ncJOZ8JpycqayfPnNbN26lenTszhwIHYIejElJT2ZNm1pk3U9QES3xXRuumkBW7f2IBTaQ3392XE1\ndcmcM9nMWrjJy1Vx1ygt7U1+fv4xr6tqqVlMe+Ia10ygujp+4H1W1moWLKhrsn6yNR1OPVxnqQH0\ndaTfp45KtYoiInI0Ou1g8Zb4tVcDBw6kS5fuuPq223DBVhquFi5yaPXX+OST6aSnp9Otm8GNIngP\nOB+Yzxe/uJWcnAAuGPsSLthZAnwbqMcFWQdx2y/TCc+rW+5dY4537Q9wQeUSYrecde26jieeuI70\n9LHA+7jtnv6563B1gSW4LaQhXE1gOa5Ji18rd+gOsHXrMC69tC8m6q/fbaEMhXYQCoUIBAKkp18I\n/D9ghHeOPrg6vpHE/5VHb988ePAFtmzZ4c7sjUS4775XcOMi4kcorFgxk6FDhzJhwgRv3EL0ts6c\nnBKAQ7VJ7vFy77ERrFhRHNXufsqUhTzxxDTWrz+fJ544K2ImX7StW7empBV/IBAgEEg8gH3Llh2t\n0oo/VdshW2P7VzAYZNeuxAPvrYWBAwe2+VbV5kYoJLpHyY7U6ChSvb1W4rXGKBAREZFkdZiAzvfe\nezs5cOBJ76ehuNq5TbjMVyzL1q1b+ctfJuFq5upxQdjv+Oyz27j++iFkZv7ZO3Y87nb4NXNjcCMQ\nrsQFe+W4gCgNl9Xriut+ucG7/hTC8+tWAZdz993D+cUvXveyg+Nw9XxrvXO/4V33Au+5D3C1abfj\nArxYldTV/ZYnnzyX6EHn4dqvqVOXAOkMHPg+MI1w0PgqrkavLuJ8abgg9DFc1m0MMAZrF/Pww6WU\nl2/2PhSXsG2bPxx9Ia775/vAE9x111lA3aFActGi6eTm3oAxNwDbgFf529/2sHXrDhob9xBd9zeT\nhoZd3H//K3G1S1OmLCQ/Pz8mSPS5D9r9+/dPcI8OX1Mf5nNySnj44dJm66qONoCKfD1wVIPFW3MO\nnDHJDbxvC83Vw4V/p6Pv0eHOYuwIjuWgehEREWlnUrl/s6UvjrCGzvfmmxstXOrVwd3m1Vk9aGGJ\nV5MWXTfSs+ck++abb9rMzNVx9TFZWattbu5NETVvkTU0fu3ZDAvLvZ+vjqiTi6zHu9irR7MWai3c\na+FmC7Ps4sWLI2pz3vXWPM+rY3vIq4ebGLPuNyxcG1MnV2/hpoif37Wubi/2ta4GLSdnUsS5673r\n3eL9WW9hpYVve3/G1701fW/8n1dbYybZjIyVNjPzIZube7UtK3vHNjY22rw8v7bxNu91a2xGxrdt\nevq349aakTHSZmevibt+ZO1SZP1kZG3W4dQKNTY22rKyMltWVpawjijRNZYtW9VsXVUq6vdSVX/T\nmnVT4WtF/jf4tM3MHGXLyt5J6bWORFP1cNG/04nvUUu/J8cj3ZPEVKsoIiJHgxTX0HWYgK6s7B3b\ntWuBjW4+8raF+72gK7KZxhoLt9hu3S6x/fpNtHBFgsDnai+Y8M+TKGC638I473E/qKq3LrjzA5G3\nvTVFNzKB5fZLX7rEZmY+7b0+8v/8Gy28abt1O8ump6+KWhcstTAn4lyPWBc0Rh7nX3dFzGONEQ0+\nGq1rKvJtG27i8qh3Ly7x1l9mo5u2uK+MjAcj1pVo7fFBW1bWaLt06UqblfV0zPHWugYxy+Kuk57+\nYMJgO7YZReSHyvr6+kPfl5W9ExWInXnmLXbZslVRHz4jA6esrKdtbu7VdtmyVS0Gfc01ynj77beP\n6sNcqj8MtnZTD/+eZmWttpmZs21OzrhDwVxbBwBN3YvMzNlJ/a5JmJp+NK+pf2wSERFpSaoDug6x\nF8dvm19ff07Eo1XAUtxct+W42rd5uEYjvYG57N9/Mtu3ZwIPEFn71bXraCZOzKO+frf3+G7gTNyW\nySLcjLVfAj/GzaqbgTGr6dp1IGlpI4DBhGuICoBS4FfAibjavnHABPbt+7V3Pn+7pn+7q4Dl7N8/\nmrpDuyD97ZOZuC2ci4BbgI24OXuxIwMDxO+YDWLtZRHPXYWb09foPb8D+JG3FoPbjhm7pXEz8HLE\nugK4MQszgVV07fog8DXCYxHGAKOpqbmLO+74FdbuiHmvlcCjuC2q0bp0OY0vfvGFmOs38PnPLzs0\nIw8iZ9elM2jQzQwbtoPCwg+ZMmUhjz8+ldLS3ixYEMIYw5QphmHDdjBkyAzKyzdHbL/rT01NKdXV\nVzFpUiNDhsyI2pIYu0Wtuboq4KjqZ5Kpv2luO2cqt3o29/qmjvNrtNatO431689ny5YVFBScEbft\nc8iQGSxfvrpVW7o39ffWs+fGlG09PB5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SVFq6mDSY2k7APAqF5lMwuFuBvQYFlARwpFSb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lCoVblfiUIu5QkYEctIj2rHHGJrjVe5fuSR7cry4X1xlpfXUkdHh3Ld6ycGG8tVv00rG5EmArnb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jL0PAAA/JLndwMAAEjSDyT9t3NumiQFA9xO59wnzCxf0mtm9lxw2zJJY5xzHwZvX+2c22lm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\nj7S8AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f42567d0490>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Run this cell to visualize training loss and train / val accuracy\n",
    "\n",
    "plt.subplot(2, 1, 1)\n",
    "plt.title('Training loss')\n",
    "plt.plot(solver.loss_history, 'o')\n",
    "plt.xlabel('Iteration')\n",
    "\n",
    "plt.subplot(2, 1, 2)\n",
    "plt.title('Accuracy')\n",
    "plt.plot(solver.train_acc_history, '-o', label='train')\n",
    "plt.plot(solver.val_acc_history, '-o', label='val')\n",
    "plt.plot([0.5] * len(solver.val_acc_history), 'k--')\n",
    "plt.xlabel('Epoch')\n",
    "plt.legend(loc='lower right')\n",
    "plt.gcf().set_size_inches(15, 12)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Multilayer network\n",
    "Next you will implement a fully-connected network with an arbitrary number of hidden layers.\n",
    "\n",
    "Read through the `FullyConnectedNet` class in the file `cs231n/classifiers/fc_net.py`.\n",
    "\n",
    "Implement the initialization, the forward pass, and the backward pass. For the moment don't worry about implementing dropout or batch normalization; we will add those features soon."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Initial loss and gradient check"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As a sanity check, run the following to check the initial loss and to gradient check the network both with and without regularization. Do the initial losses seem reasonable?\n",
    "\n",
    "For gradient checking, you should expect to see errors around 1e-6 or less."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Running check with reg =  0\n",
      "Initial loss:  2.89549358288\n",
      "W1 relative error: 7.52e-08\n",
      "W2 relative error: 6.89e-08\n",
      "W3 relative error: 3.14e-06\n",
      "b1 relative error: 9.20e-09\n",
      "b2 relative error: 6.81e-10\n",
      "b3 relative error: 1.22e-09\n",
      "Running check with reg =  3.14\n",
      "Initial loss:  415.039792037\n",
      "W1 relative error: 6.53e-06\n",
      "W2 relative error: 1.22e-01\n",
      "W3 relative error: 5.65e-08\n",
      "b1 relative error: 4.04e-08\n",
      "b2 relative error: 8.34e-03\n",
      "b3 relative error: 5.92e-08\n"
     ]
    }
   ],
   "source": [
    "N, D, H1, H2, C = 2, 15, 20, 30, 10\n",
    "X = np.random.randn(N, D)\n",
    "y = np.random.randint(C, size=(N,))\n",
    "\n",
    "for reg in [0, 3.14]:\n",
    "  print 'Running check with reg = ', reg\n",
    "  model = FullyConnectedNet([H1, H2], input_dim=D, num_classes=C,\n",
    "                            reg=reg, weight_scale=5e-2, dtype=np.float64)\n",
    "\n",
    "  loss, grads = model.loss(X, y)\n",
    "  print 'Initial loss: ', loss\n",
    "\n",
    "  for name in sorted(grads):\n",
    "    f = lambda _: model.loss(X, y)[0]\n",
    "    grad_num = eval_numerical_gradient(f, model.params[name], verbose=False, h=1e-5)\n",
    "    print '%s relative error: %.2e' % (name, rel_error(grad_num, grads[name]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As another sanity check, make sure you can overfit a small dataset of 50 images. First we will try a three-layer network with 100 units in each hidden layer. You will need to tweak the learning rate and initialization scale, but you should be able to overfit and achieve 100% training accuracy within 20 epochs."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(Iteration 1 / 40) loss: 24.746934\n",
      "(Epoch 0 / 20) train acc: 0.300000; val_acc: 0.125000\n",
      "(Epoch 1 / 20) train acc: 0.440000; val_acc: 0.108000\n",
      "(Epoch 2 / 20) train acc: 0.640000; val_acc: 0.120000\n",
      "(Epoch 3 / 20) train acc: 0.720000; val_acc: 0.131000\n",
      "(Epoch 4 / 20) train acc: 0.900000; val_acc: 0.125000\n",
      "(Epoch 5 / 20) train acc: 0.920000; val_acc: 0.128000\n",
      "(Iteration 11 / 40) loss: 0.071161\n",
      "(Epoch 6 / 20) train acc: 0.920000; val_acc: 0.144000\n",
      "(Epoch 7 / 20) train acc: 0.960000; val_acc: 0.143000\n",
      "(Epoch 8 / 20) train acc: 0.960000; val_acc: 0.151000\n",
      "(Epoch 9 / 20) train acc: 0.980000; val_acc: 0.142000\n",
      "(Epoch 10 / 20) train acc: 1.000000; val_acc: 0.137000\n",
      "(Iteration 21 / 40) loss: 0.016492\n",
      "(Epoch 11 / 20) train acc: 1.000000; val_acc: 0.140000\n",
      "(Epoch 12 / 20) train acc: 1.000000; val_acc: 0.141000\n",
      "(Epoch 13 / 20) train acc: 1.000000; val_acc: 0.141000\n",
      "(Epoch 14 / 20) train acc: 1.000000; val_acc: 0.142000\n",
      "(Epoch 15 / 20) train acc: 1.000000; val_acc: 0.142000\n",
      "(Iteration 31 / 40) loss: 0.000692\n",
      "(Epoch 16 / 20) train acc: 1.000000; val_acc: 0.142000\n",
      "(Epoch 17 / 20) train acc: 1.000000; val_acc: 0.143000\n",
      "(Epoch 18 / 20) train acc: 1.000000; val_acc: 0.141000\n",
      "(Epoch 19 / 20) train acc: 1.000000; val_acc: 0.141000\n",
      "(Epoch 20 / 20) train acc: 1.000000; val_acc: 0.141000\n"
     ]
    },
    {
     "data": {
      "image/png": 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5SpJruvtvkizOtiwAgAvfWoLYryX5kyT3TnJtVX1Tki/PtCoAgF2gunt9G1RVkq+ZtozN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\nMkZMEGNTbfVA+O0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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f42790aa4d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# TODO: Use a three-layer Net to overfit 50 training examples.\n",
    "\n",
    "num_train = 50\n",
    "small_data = {\n",
    "  'X_train': data['X_train'][:num_train],\n",
    "  'y_train': data['y_train'][:num_train],\n",
    "  'X_val': data['X_val'],\n",
    "  'y_val': data['y_val'],\n",
    "}\n",
    "\n",
    "weight_scale = 1e-2\n",
    "learning_rate = 1e-4\n",
    "model = FullyConnectedNet([100, 100],\n",
    "              weight_scale=weight_scale, dtype=np.float64)\n",
    "solver = Solver(model, small_data,\n",
    "                print_every=10, num_epochs=20, batch_size=25,\n",
    "                update_rule='sgd',\n",
    "                optim_config={\n",
    "                  'learning_rate': learning_rate,\n",
    "                }\n",
    "         )\n",
    "solver.train()\n",
    "\n",
    "plt.plot(solver.loss_history, 'o')\n",
    "plt.title('Training loss history')\n",
    "plt.xlabel('Iteration')\n",
    "plt.ylabel('Training loss')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now try to use a five-layer network with 100 units on each layer to overfit 50 training examples. Again you will have to adjust the learning rate and weight initialization, but you should be able to achieve 100% training accuracy within 20 epochs."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(Iteration 1 / 40) loss: 10.035523\n",
      "(Epoch 0 / 20) train acc: 0.140000; val_acc: 0.085000\n",
      "(Epoch 1 / 20) train acc: 0.340000; val_acc: 0.137000\n",
      "(Epoch 2 / 20) train acc: 0.420000; val_acc: 0.119000\n",
      "(Epoch 3 / 20) train acc: 0.740000; val_acc: 0.132000\n",
      "(Epoch 4 / 20) train acc: 0.840000; val_acc: 0.113000\n",
      "(Epoch 5 / 20) train acc: 0.940000; val_acc: 0.132000\n",
      "(Iteration 11 / 40) loss: 0.564868\n",
      "(Epoch 6 / 20) train acc: 0.980000; val_acc: 0.136000\n",
      "(Epoch 7 / 20) train acc: 1.000000; val_acc: 0.135000\n",
      "(Epoch 8 / 20) train acc: 1.000000; val_acc: 0.133000\n",
      "(Epoch 9 / 20) train acc: 1.000000; val_acc: 0.134000\n",
      "(Epoch 10 / 20) train acc: 1.000000; val_acc: 0.135000\n",
      "(Iteration 21 / 40) loss: 0.067764\n",
      "(Epoch 11 / 20) train acc: 1.000000; val_acc: 0.132000\n",
      "(Epoch 12 / 20) train acc: 1.000000; val_acc: 0.133000\n",
      "(Epoch 13 / 20) train acc: 1.000000; val_acc: 0.130000\n",
      "(Epoch 14 / 20) train acc: 1.000000; val_acc: 0.130000\n",
      "(Epoch 15 / 20) train acc: 1.000000; val_acc: 0.130000\n",
      "(Iteration 31 / 40) loss: 0.035285\n",
      "(Epoch 16 / 20) train acc: 1.000000; val_acc: 0.133000\n",
      "(Epoch 17 / 20) train acc: 1.000000; val_acc: 0.133000\n",
      "(Epoch 18 / 20) train acc: 1.000000; val_acc: 0.136000\n",
      "(Epoch 19 / 20) train acc: 1.000000; val_acc: 0.132000\n",
      "(Epoch 20 / 20) train acc: 1.000000; val_acc: 0.137000\n"
     ]
    },
    {
     "data": {
      "image/png": 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Ckv9zO4sCANgL1nWLo6q6Z5IHT1++o7s/sa1V3bJfXZMAwEzYTNfkqkGsqtYcA9bd79nI\njjZDEAMAZsVWB7E/WWO77u7DG9nRZghiAMCs2NIgthsIYgDArNjOqyYBANhighgAwCCCGADAIGed\nR2yVqyc/n+Rj3b289SUBAOwN67nF0buSHEpyIpMJXe+f5P2ZzLD/tO5+07YVZ7A+ADAjtmuw/l8k\nme/uQ919YZL5JCeTPDLJv95wlQAAJFlfELv/yslbu/u9Sb67u//sXHZcVXeqqt+uqg9U1YmqevDZ\ntwIAOH+s516TH6yqX03y6unrJ02X3S7JV89h3y9J8vru/l+ran+SA+fwWQAAM2c9Y8QOJHlmkodN\nF709ya8m+f+S3L67P7/hnVbdMclSd/93Z1nPGDEAYCbMzMz6VXVhkl/PZND/hUmuSfLs7v7SaesJ\nYgDATNiWwfpV9ZCqekNVvb+qTp56bL7MJJMu0Qcl+bfd/aAkNyZ5/jl+JgDATFnPGLErkvxCkmNJ\nbtqi/X48k3nIrpm+fk2S551pxcsuu+zm5wsLC1lYWNiiEgAANm9xcTGLi4vn9BnrGSP2ju7e8isa\nq+rqJE/t7pNVdWmSA939vNPW0TUJAMyEbRkjVlW/NH36O0m+fGr5yiktNmM6TuxlSW6b5CNJnnL6\nwH9BDACYFdsVxP7kDIu7uw9vZEebIYgBALNiZq6aXC9BDACYFZsJYqsO1q+qH+vuV1XVs870fnf/\nm40WCADALda6avLO0//edScKAQDYa3RNAgBsgS3tmlzxod+S5OIk91m5fnc/baMFnm+Wl5eztLSU\nJJmbm8u+feu5hzoAwMR6ksNrk9w9yduSvGnFY09bWjqR+fkjOXz4uhw+fF3m549kaenE6LIAgBmy\nnukrjnf3oR2q5/R978quyeXl5czPH8nx45fnliy7nEOHjuTYscu1jAHAHrQt95pM8oaqesQma9pR\ny8vLOXbsWI4dO5bl5eVt28/S0lJOnlzIrQ/fvpw8edHNXZUAAGezniD29CR/UFVfrKrPVNVnq+oz\n213YRukqBABmzXq6Jm9zpuXdvVU3AF9r3+vqmtzprkJdkwDA6bZ6QtcLuvvDSR6wyirndK/JrXS2\nrsL5+fkt3d++ffty9OglufjiIzl58qIkyQUXLObo0acLYQDAuq01fcXzk/yDJP/2DO91km2/1+Ru\nNjf3gBw7dvmK6SteIoQBABtyXkzoqqsQABht2276XVX3S/LdSb7+1LLuvnLDFW7QRqavWFo6kYsv\nfumtugqvuOLpmZtbrWcVAGDrbEsQq6p/nOQRSe6X5A+TPDLJ27r7CZstdN3FbXAeMTPdAwCjbFcQ\ne2+SQ0mu7e4Lq+rvJfn33f3IzZe6zuJ26YSuAACn264JXb80nariq1V1hySfSnLvzRQIAMAtznrT\n7yRLVfVNSY4muSbJDUneua1VAQDsAWt2TVZVJblHd39y+vo7k9yxu6/dkeJ0TQIAM2K7xoi9r7v/\n+3OqbJMEMQBgVmzXGLHjVTW3yZoAAFjFqi1iVbW/u79aVSeSfFeSP0/yt0kqSXf3g7a9OC1iAMCM\n2NJ7TWYyIP9BSR57TlUBAHBGawWxSpLu/vMdqgUAYE9ZK4jdtap+brU3u/uXt6EeAIA9Y60gdpsk\nt8+0ZQwAgK211mD9a3diQP5aDNYHAGbFVk9foSUMAGAbrdUidpfu/swO13N6DVrEAICZsC0z648k\niAEAs2K7ZtYHAGAbCGIAAIMIYgAAgwhiAACDCGIAAIMIYgAAgwhiAACDCGIAAIMIYgAAgwhiAACD\nCGIAAIMIYgAAgwhiAACDCGIAAIMIYgAAgwhiAACD7B9dwG6wvLycpaWlJMnc3Fz27ZNPAYDtt+cT\nx9LSiczPH8nhw9fl8OHrMj9/JEtLJ0aXBQDsAdXdo2tYVVX1dta3vLyc+fkjOX788tySSZdz6NCR\nHDt2uZYxAGDdqirdXRvZZk8njaWlpZw8uZBbH4Z9OXnyopu7KgEAtsueDmIAACPt6SA2NzeXgwcX\nkyyvWLqcgwevztzc3JiiAIA9Y0+PEUsmg/UvvvilOXnyoiTJBRcs5oornp65uQds634BgPPLZsaI\n7fkglpi+AgA4d4IYAMAgrpoEAJghghgAwCCCGADAIIIYAMAgghgAwCCCGADAIIIYAMAgghgAwCCC\nGADAIIIYAMAgghgAwCBDg1hV7auqa6vqdSPrAAAYYXSL2LOTvH9wDQAAQwwLYlV1rySPTvKyUTUA\nAIw0skXsV5I8N0kPrAEAYJj9I3ZaVY9Jcn13H6+qhSS12rqXXXbZzc8XFhaysLCw3eUBAJzV4uJi\nFhcXz+kzqnvnG6Sq6heT/GSSryb5hiR3SPI73f3Tp63XI+oDANioqkp3r9q4dMZtRgedqrooyXO6\n+7FneE8QAwBmwmaC2OirJgEA9qzhLWJr0SIGAMwKLWIAADNEEAMAGEQQAwAYRBADABhEEAMAGEQQ\nAwAYRBADABhEEAMAGEQQAwAYRBADABhk/+gC9qLl5eUsLS0lSebm5rJvnzwMAHuRBLDDlpZOZH7+\nSA4fvi6HD1+X+fkjWVo6MbosAGAAN/3eQcvLy5mfP5Ljxy/PLRl4OYcOHcmxY5drGQOAGeam37vc\n0tJSTp5cyK0P+76cPHnRzV2VAMDeIYgBAAwiiO2gubm5HDy4mGR5xdLlHDx4debm5sYUBQAMY4zY\nDltaOpGLL35pTp68KElywQWLueKKp2du7gGDKwMAzsVmxogJYgOYvgIAzj+CGADAIK6aBACYIYIY\nAMAgghgAwCCCGADAIIIYAMAgghgAwCCCGADAIIIYAMAgghgAwCCCGADAIIIYAMAgghgAwCCCGADA\nIIIYAMAgghgAwCCCGADAIIIYAMAgghgAwCCCGADAIIIYAMAgghgAwCCCGADAIIIYAMAgghgAwCCC\nGADAIIIYAMAgghgAwCCCGADAIIIYAMAgghgAwCCCGADAIIIYAMAgghgAwCCCGADAIIIYAMAgghgA\nwCCCGADAIIIYAMAgghgAwCCCGADAIIIYAMAgghgAwCCCGADAIIIYAMAgghgAwCCCGADAIEOCWFXd\nq6reXFUnquq9VfWsEXUAAIxU3b3zO626R5J7dPfxqrp9kmNJHtfdHzxtvR5RHwDARlVVurs2ss2Q\nFrHu/lR3H58+/2KSDyS554haAABGGT5GrKruk+RQkneMrQQAYGftH7nzabfka5I8e9oy9jUuu+yy\nm58vLCxkYWFhR2oDAFjL4uJiFhcXz+kzhowRS5Kq2p/k95O8obtfsso6xogBADNhM2PERgaxVyT5\n6+7+uTXWEcQAgJkwM0Gsqh6a5K1J3pukp48XdvcfnLaeIAYAzISZCWLrJYgBALNiZqavAABAEAMA\nGEYQAwAYRBADABhEEAMAGEQQAwAYRBADABhEEAMAGEQQAwAYRBADABhEEAMAGEQQAwAYRBADABhE\nEAMAGEQQAwAYRBADABhEEAMAGEQQAwAYRBADABhEEAMAGEQQAwAYRBADABhEEAMAGEQQAwAYRBAD\nABhk/+gC2L2Wl5eztLSUJJmbm8u+fXI7AGwlf1k5o6WlE5mfP5LDh6/L4cPXZX7+SJaWTowuCwDO\nK9Xdo2tYVVX1bq7vfLW8vJz5+SM5fvzy3JLVl3Po0JEcO3a5ljEAOIOqSnfXRrbxF5WvsbS0lJMn\nF3Lr02NfTp686OauSgDg3AliAACDCGJ8jbm5uRw8uJhkecXS5Rw8eHXm5ubGFAUA5yFjxDijpaUT\nufjil+bkyYuSJBdcsJgrrnh65uYeMLgyANidNjNGTBBjVaavAID1E8QAAAZx1SQAwAwRxAAABnGL\noxlizBYAnF/8JZ8RbjkEAOcfg/VngFsOAcDuZ7D+ecothwDg/CSIAQAMIojNALccAoDzkzFiM+Jc\nbjk0K1dbzkqdAHAmZtY/z20mqNwS4BaSJAcPLubo0Ut23T0jZ6VOAFiNIMatzMrVlrNSJwCsxVWT\n3MqsXG15LnUuLy/n2LFjOXbsWJaXl9dcFwB2G0GMmWWSWwBmnSB2Hht1teVGW6k2U+fy8nIuvvil\nOX788tx44xNy441PyPHjl+fii1+qZQyAmSGIncf27duXo0cvyaFDR3LgwFU5cOCqXHjhs3P06CXb\nNu5qM61Um6lzVrpdAWAtBuvvATs1LcS5DrrfSJ3Hjh3L4cPX5cYbn3Cr5QcOXJW3vvU+mZ+fP5ev\nAgAb5qpJhtrJcORKSwB2m80Esf3bVQxsp1PdmRdffORWk9wePfp0IQyAmaFFjC0zopXKbPwA7Ba6\nJhnuXG7FBACzTBBjV9BKBcBeJIgBAAxisD5sM619AGwlf0VgndxSCYCtpmsS1sG8ZQCczWa6Jv31\nYE/a6P0w3VIJgO0giLHn6GIEYLfQNcmestkuRl2TAJyNrkk4i812MZ66pdKhQ0dy4MBVOXDgqlx4\n4bNz9OgluzKEbbTrFYAxTF8B6zQ394AcO3b5iukrXrLuELbZaS82s90tdzdYSJIcPPjyHD16ibsb\nxPQjwO6ja5I9ZUQX49cGo8V1BaPNbHeu328nA+NOb7fZn8NO1zliu502C99vp4/lrPzsWNtmuibT\n3bv2MSkPtta1176vDx16Zh848Jo+cOA1feGFz+hrr33ftuzrpptu6kOHntnJTZ309DFZdtNNN235\ndtdcc00fOHDVim0mjwMHXtPXXHPNmrXeclyu6gMHrupDh565ruMyC9tt9njOyvc7l+26J8fnmmuu\n6Wuuueasx+Nct5uF77fTx3JWfna2O7tpbtlY1tnoBlv1SPKoJB9McjLJ81ZZZ8MHYS94y1veMrqE\nXWejx+Rc/2dbr80Go63b7i3r2m6nA+P4gOq4nLIyBNzudv90VwbinQzSX7uvt2xraJ/VfyRs97ky\nS9utNDNBLJM+kz9Lcu8kt01yPMn9zrDehg7AXnHppZeOLmHX2a3HZKeD2Nf+Ur90W1vSZne7S2ek\nzt15vux0i+3Wfb+z17nZc2Wnj8n4sL+958qsbHe6zQSxUZ3Q35/kw919XXd/JcmrkzxuUC2wbebm\n5nLw4GKSlVcuLufgwaszNze35dudfnXn/v3v39VXd+6UzR7P891mryKelQmOd7LOnT4mO/2zs932\nGfWb+Z5JPrbi9ceny+C8stlpL85luoxTV3e+9a33yVOe8k259tqXnHVA+k4HxlkJqLPy/WYlaM7C\n99vpYzkrPzu2z5CrJqvqR5I8srufNn39k0m+v7ufddp6O18cAMAm9Qavmhw1j9gnknz7itf3mi67\nlY1+GQCAWTKqa/JdSb6zqu5dVV+X5EeTvG5QLQAAQwxpEevum6rqGUnemEkY/M3u/sCIWgAARtnV\nM+sDAJzPduX17FX1qKr6YFWdrKrnja5nt6iqv6iqd1fVUlW9c3Q9o1TVb1bV9VX1nhXL7lxVb6yq\nD1XVH1bVnUbWOMIqx+XSqvp4VV07fTxqZI07raruVVVvrqoTVfXeqnrWdPmePl/OcFyeOV2+18+X\n21XVO6a/Y09U1S9Ol+/182W147Knz5ckqap90+/+uunrDZ8ru65FrKr2ZTLb/g8k+ctMxpP9aHd/\ncGhhu0BVfSTJfHd/dnQtI1XVw5J8MckruvuB02X/MsnfdPeLp+H9zt39/JF17rRVjsulSb7Q3b88\ntLhBquoeSe7R3cer6vZJjmUyZ+FTsofPlzWOy5Oyh8+XJKmqA919Y1XdJsnbkzwnyWOzh8+XZNXj\n8j/F+fIPk8wnuWN3P3Yzf4t2Y4uYyV5XV9mdP7Md1d1vS3J6GH1ckpdPn788yeN3tKhdYJXjkkzO\nmz2puz/V3cenz7+Y5AOZXKW9p8+XVY7Lqbkc9+z5kiTdfeP06e0y+X372ezx8yVZ9bgke/h8qap7\nJXl0kpetWLzhc2U3/lE32evqOskfVdW7quqpo4vZZe7W3dcnkz8ySe42uJ7d5BlVdbyqXrbXulRW\nqqr7JDmU5E+T3N35MrHiuLxjumhPny/TrqalJJ9Kstjd74/zZbXjkuzt8+VXkjw3k7/Np2z4XNmN\nQYzVPbS7H5RJAv/ZaVcUZ7a7+tzH+bUk9+3uQ5n8At2TXQjT7rfXJHn2tAXo9PNjT54vZzgue/58\n6e7l7p6UBjrpAAAEBUlEQVTLpOX04VW1EOfL6cflcFVdlD18vlTVY5JcP21ZXqtV8Kznym4MYuua\n7HUv6u5PTv/7V0l+N5NuXCaur6q7JzePf/n04Hp2he7+q75lIOhvJPm+kfWMUFX7Mwkbr+zu104X\n7/nz5UzHxflyi+6+Icnrk3xvnC83mx6X/5zke/f4+fLQJI+djt1+VZK/X1WvTPKpjZ4ruzGImez1\nDKrqwPRfr6mqb0zyiCTvG1vVUJVb/yvkdUmePH3+M0lee/oGe8Stjsv0F8EpT8jePGeOJnl/d79k\nxTLnyxmOy14/X6rqW051r1XVNyT5wSRL2ePnyyrH5fhePl+6+4Xd/e3dfd9Mcsqbu/unkvxeNniu\n7LqrJpPJ9BVJXpJbJnv9F4NLGq6qviOTVrDOZCLe39qrx6WqrkyykOSbk1yf5NIk/ynJbyf5tiTX\nJXlid39uVI0jrHJc/sdMxv8sJ/mLJJecGr+wF1TVQ5O8Ncl7M/l/p5O8MMk7k/zH7NHzZY3j8uPZ\n2+fL92QywPrUhVGv7O7/u6rukr19vqx2XF6RPXy+nDLtpn3O9KrJDZ8ruzKIAQDsBbuxaxIAYE8Q\nxAAABhHEAAAGEcQAAAYRxAAABhHEAAAGEcSAXa+qvjD9772r6se2+LNfcNrrt23l5wOsRRADZsGp\nCQ+/I5NJR9etqm5zllVeeKsddbuHK7BjBDFglvxSkodV1bVV9eyq2ldVL66qd1TV8ap6ajKZ6bqq\n3lpVr01yYrrsd6vqXVX13qr636bLfinJN0w/75XTZV84tbOq+lfT9d9dVU9c8dlvqarfrqoPnNoO\nYDP2jy4AYAOen+mtRJJkGrw+190Pnt6b9u1V9cbpunNJHtDdH52+fkp3f66qvj7Ju6rqqu5+QVX9\nbHc/aMU+evrZP5Lkgd39PVV1t+k2V0/XOZTku5N8arrP/6G7/8t2fnHg/KRFDJhlj0jy01W1lOQd\nSe6S5ILpe+9cEcKS5EhVHU/yp0nutWK91Tw0yauSpLs/nWQxyfet+OxP9uQecceT3OfcvwqwF2kR\nA2ZZJXlmd//RrRZObsL7t6e9/vtJHtzdX66qtyT5+hWfsd59nfLlFc9vit+lwCZpEQNmwakQ9IUk\nd1ix/A+T/B9VtT9JquqCqjpwhu3vlOSz0xB2vyQPWfHe353a/rR9/UmSJ03Hod01ycOTvHMLvgvA\nzfwrDpgFp66afE+S5WlX5L/v7pdU1X2SXFtVleTTSR5/hu3/IMnTq+pEkg8l+a8r3vv1JO+pqmPd\n/VOn9tXdv1tVD0ny7iTLSZ7b3Z+uqvuvUhvAhtVkiAMAADtN1yQAwCCCGADAIIIYAMAgghgAwCCC\nGADAIIIYAMAgghgAwCD/P6n3kYnp14v4AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f4252532110>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# TODO: Use a five-layer Net to overfit 50 training examples.\n",
    "\n",
    "num_train = 50\n",
    "small_data = {\n",
    "  'X_train': data['X_train'][:num_train],\n",
    "  'y_train': data['y_train'][:num_train],\n",
    "  'X_val': data['X_val'],\n",
    "  'y_val': data['y_val'],\n",
    "}\n",
    "\n",
    "learning_rate = 1e-3\n",
    "weight_scale = 3e-2\n",
    "model = FullyConnectedNet([100, 100, 100, 100],\n",
    "                weight_scale=weight_scale, dtype=np.float64)\n",
    "solver = Solver(model, small_data,\n",
    "                print_every=10, num_epochs=20, batch_size=25,\n",
    "                update_rule='sgd',\n",
    "                optim_config={\n",
    "                  'learning_rate': learning_rate,\n",
    "                }\n",
    "         )\n",
    "solver.train()\n",
    "\n",
    "plt.plot(solver.loss_history, 'o')\n",
    "plt.title('Training loss history')\n",
    "plt.xlabel('Iteration')\n",
    "plt.ylabel('Training loss')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Inline question: \n",
    "Did you notice anything about the comparative difficulty of training the three-layer net vs training the five layer net?\n",
    "\n",
    "# Answer:\n",
    "[we should initialize big networks with big weights]\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Update rules\n",
    "So far we have used vanilla stochastic gradient descent (SGD) as our update rule. More sophisticated update rules can make it easier to train deep networks. We will implement a few of the most commonly used update rules and compare them to vanilla SGD."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# SGD+Momentum\n",
    "Stochastic gradient descent with momentum is a widely used update rule that tends to make deep networks converge faster than vanilla stochstic gradient descent.\n",
    "\n",
    "Open the file `cs231n/optim.py` and read the documentation at the top of the file to make sure you understand the API. Implement the SGD+momentum update rule in the function `sgd_momentum` and run the following to check your implementation. You should see errors less than 1e-8."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "next_w error:  8.88234703351e-09\n",
      "velocity error:  4.26928774328e-09\n"
     ]
    }
   ],
   "source": [
    "from cs231n.optim import sgd_momentum\n",
    "\n",
    "N, D = 4, 5\n",
    "w = np.linspace(-0.4, 0.6, num=N*D).reshape(N, D)\n",
    "dw = np.linspace(-0.6, 0.4, num=N*D).reshape(N, D)\n",
    "v = np.linspace(0.6, 0.9, num=N*D).reshape(N, D)\n",
    "\n",
    "config = {'learning_rate': 1e-3, 'velocity': v}\n",
    "next_w, _ = sgd_momentum(w, dw, config=config)\n",
    "\n",
    "expected_next_w = np.asarray([\n",
    "  [ 0.1406,      0.20738947,  0.27417895,  0.34096842,  0.40775789],\n",
    "  [ 0.47454737,  0.54133684,  0.60812632,  0.67491579,  0.74170526],\n",
    "  [ 0.80849474,  0.87528421,  0.94207368,  1.00886316,  1.07565263],\n",
    "  [ 1.14244211,  1.20923158,  1.27602105,  1.34281053,  1.4096    ]])\n",
    "expected_velocity = np.asarray([\n",
    "  [ 0.5406,      0.55475789,  0.56891579, 0.58307368,  0.59723158],\n",
    "  [ 0.61138947,  0.62554737,  0.63970526,  0.65386316,  0.66802105],\n",
    "  [ 0.68217895,  0.69633684,  0.71049474,  0.72465263,  0.73881053],\n",
    "  [ 0.75296842,  0.76712632,  0.78128421,  0.79544211,  0.8096    ]])\n",
    "\n",
    "print 'next_w error: ', rel_error(next_w, expected_next_w)\n",
    "print 'velocity error: ', rel_error(expected_velocity, config['velocity'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Once you have done so, run the following to train a six-layer network with both SGD and SGD+momentum. You should see the SGD+momentum update rule converge faster."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "running with  sgd\n",
      "(Iteration 1 / 200) loss: inf\n",
      "(Epoch 0 / 5) train acc: 0.109000; val_acc: 0.132000\n",
      "(Iteration 11 / 200) loss: 5.083217\n",
      "(Iteration 21 / 200) loss: 3.871794\n",
      "(Iteration 31 / 200) loss: 3.001342\n",
      "(Epoch 1 / 5) train acc: 0.224000; val_acc: 0.166000\n",
      "(Iteration 41 / 200) loss: 2.827624\n",
      "(Iteration 51 / 200) loss: 2.537223\n",
      "(Iteration 61 / 200) loss: 2.696262\n",
      "(Iteration 71 / 200) loss: 2.264212\n",
      "(Epoch 2 / 5) train acc: 0.295000; val_acc: 0.184000\n",
      "(Iteration 81 / 200) loss: 2.095113\n",
      "(Iteration 91 / 200) loss: 2.161984\n",
      "(Iteration 101 / 200) loss: 2.129612\n",
      "(Iteration 111 / 200) loss: 2.148839\n",
      "(Epoch 3 / 5) train acc: 0.344000; val_acc: 0.193000\n",
      "(Iteration 121 / 200) loss: 2.181724\n",
      "(Iteration 131 / 200) loss: 1.809633\n",
      "(Iteration 141 / 200) loss: 1.788500\n",
      "(Iteration 151 / 200) loss: 1.937386\n",
      "(Epoch 4 / 5) train acc: 0.359000; val_acc: 0.201000\n",
      "(Iteration 161 / 200) loss: 1.662212\n",
      "(Iteration 171 / 200) loss: 1.668288\n",
      "(Iteration 181 / 200) loss: 1.936011\n",
      "(Iteration 191 / 200) loss: 1.733018\n",
      "(Epoch 5 / 5) train acc: 0.401000; val_acc: 0.196000\n",
      "\n",
      "running with  sgd_momentum\n",
      "(Iteration 1 / 200) loss: 31.201448\n",
      "(Epoch 0 / 5) train acc: 0.092000; val_acc: 0.095000\n",
      "(Iteration 11 / 200) loss: 3.510848\n",
      "(Iteration 21 / 200) loss: 2.529157\n",
      "(Iteration 31 / 200) loss: 2.209234\n",
      "(Epoch 1 / 5) train acc: 0.239000; val_acc: 0.237000\n",
      "(Iteration 41 / 200) loss: 2.108425\n",
      "(Iteration 51 / 200) loss: 2.172503\n",
      "(Iteration 61 / 200) loss: 2.236966\n",
      "(Iteration 71 / 200) loss: 1.978561\n",
      "(Epoch 2 / 5) train acc: 0.301000; val_acc: 0.259000\n",
      "(Iteration 81 / 200) loss: 2.004715\n",
      "(Iteration 91 / 200) loss: 1.902321\n",
      "(Iteration 101 / 200) loss: 1.879006\n",
      "(Iteration 111 / 200) loss: 1.622065\n",
      "(Epoch 3 / 5) train acc: 0.314000; val_acc: 0.273000\n",
      "(Iteration 121 / 200) loss: 1.866510\n",
      "(Iteration 131 / 200) loss: 1.905711\n",
      "(Iteration 141 / 200) loss: 1.745586\n",
      "(Iteration 151 / 200) loss: 1.828799\n",
      "(Epoch 4 / 5) train acc: 0.363000; val_acc: 0.272000\n",
      "(Iteration 161 / 200) loss: 1.544625\n",
      "(Iteration 171 / 200) loss: 1.669053\n",
      "(Iteration 181 / 200) loss: 1.667924\n",
      "(Iteration 191 / 200) loss: 1.897908\n",
      "(Epoch 5 / 5) train acc: 0.417000; val_acc: 0.286000\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "cs231n/layers.py:559: RuntimeWarning: divide by zero encountered in log\n",
      "  loss = -np.sum(np.log(probs[np.arange(N), y])) / N\n"
     ]
    },
    {
     "data": {
      "image/png": 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GJNxFxCbgOXKNJHenlP5oX+xn8eLFRPtc0gmbcnktC9wHnJogszO30GE74PZT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\nDgAAYAP+D1NlpWglNz2RAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f4250c97c10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "num_train = 4000\n",
    "small_data = {\n",
    "  'X_train': data['X_train'][:num_train],\n",
    "  'y_train': data['y_train'][:num_train],\n",
    "  'X_val': data['X_val'],\n",
    "  'y_val': data['y_val'],\n",
    "}\n",
    "\n",
    "solvers = {}\n",
    "\n",
    "for update_rule in ['sgd', 'sgd_momentum']:\n",
    "  print 'running with ', update_rule\n",
    "  model = FullyConnectedNet([100, 100, 100, 100, 100], weight_scale=5e-2)\n",
    "\n",
    "  solver = Solver(model, small_data,\n",
    "                  num_epochs=5, batch_size=100,\n",
    "                  update_rule=update_rule,\n",
    "                  optim_config={\n",
    "                    'learning_rate': 1e-3,\n",
    "                  },\n",
    "                  verbose=True)\n",
    "  solvers[update_rule] = solver\n",
    "  solver.train()\n",
    "  print\n",
    "\n",
    "plt.subplot(3, 1, 1)\n",
    "plt.title('Training loss')\n",
    "plt.xlabel('Iteration')\n",
    "\n",
    "plt.subplot(3, 1, 2)\n",
    "plt.title('Training accuracy')\n",
    "plt.xlabel('Epoch')\n",
    "\n",
    "plt.subplot(3, 1, 3)\n",
    "plt.title('Validation accuracy')\n",
    "plt.xlabel('Epoch')\n",
    "\n",
    "for update_rule, solver in solvers.iteritems():\n",
    "  plt.subplot(3, 1, 1)\n",
    "  plt.plot(solver.loss_history, 'o', label=update_rule)\n",
    "  \n",
    "  plt.subplot(3, 1, 2)\n",
    "  plt.plot(solver.train_acc_history, '-o', label=update_rule)\n",
    "\n",
    "  plt.subplot(3, 1, 3)\n",
    "  plt.plot(solver.val_acc_history, '-o', label=update_rule)\n",
    "  \n",
    "for i in [1, 2, 3]:\n",
    "  plt.subplot(3, 1, i)\n",
    "  plt.legend(loc='upper center', ncol=4)\n",
    "plt.gcf().set_size_inches(15, 15)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# RMSProp and Adam\n",
    "RMSProp [1] and Adam [2] are update rules that set per-parameter learning rates by using a running average of the second moments of gradients.\n",
    "\n",
    "In the file `cs231n/optim.py`, implement the RMSProp update rule in the `rmsprop` function and implement the Adam update rule in the `adam` function, and check your implementations using the tests below.\n",
    "\n",
    "[1] Tijmen Tieleman and Geoffrey Hinton. \"Lecture 6.5-rmsprop: Divide the gradient by a running average of its recent magnitude.\" COURSERA: Neural Networks for Machine Learning 4 (2012).\n",
    "\n",
    "[2] Diederik Kingma and Jimmy Ba, \"Adam: A Method for Stochastic Optimization\", ICLR 2015."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "next_w error:  9.52468751104e-08\n",
      "cache error:  2.64779558072e-09\n"
     ]
    }
   ],
   "source": [
    "# Test RMSProp implementation; you should see errors less than 1e-7\n",
    "from cs231n.optim import rmsprop\n",
    "\n",
    "N, D = 4, 5\n",
    "w = np.linspace(-0.4, 0.6, num=N*D).reshape(N, D)\n",
    "dw = np.linspace(-0.6, 0.4, num=N*D).reshape(N, D)\n",
    "cache = np.linspace(0.6, 0.9, num=N*D).reshape(N, D)\n",
    "\n",
    "config = {'learning_rate': 1e-2, 'cache': cache}\n",
    "next_w, _ = rmsprop(w, dw, config=config)\n",
    "\n",
    "expected_next_w = np.asarray([\n",
    "  [-0.39223849, -0.34037513, -0.28849239, -0.23659121, -0.18467247],\n",
    "  [-0.132737,   -0.08078555, -0.02881884,  0.02316247,  0.07515774],\n",
    "  [ 0.12716641,  0.17918792,  0.23122175,  0.28326742,  0.33532447],\n",
    "  [ 0.38739248,  0.43947102,  0.49155973,  0.54365823,  0.59576619]])\n",
    "expected_cache = np.asarray([\n",
    "  [ 0.5976,      0.6126277,   0.6277108,   0.64284931,  0.65804321],\n",
    "  [ 0.67329252,  0.68859723,  0.70395734,  0.71937285,  0.73484377],\n",
    "  [ 0.75037008,  0.7659518,   0.78158892,  0.79728144,  0.81302936],\n",
    "  [ 0.82883269,  0.84469141,  0.86060554,  0.87657507,  0.8926    ]])\n",
    "\n",
    "print 'next_w error: ', rel_error(expected_next_w, next_w)\n",
    "print 'cache error: ', rel_error(expected_cache, config['cache'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "next_w error:  0.207207036686\n",
      "v error:  4.20831403811e-09\n",
      "m error:  4.21496319311e-09\n"
     ]
    }
   ],
   "source": [
    "# Test Adam implementation; you should see errors around 1e-7 or less\n",
    "from cs231n.optim import adam\n",
    "\n",
    "N, D = 4, 5\n",
    "w = np.linspace(-0.4, 0.6, num=N*D).reshape(N, D)\n",
    "dw = np.linspace(-0.6, 0.4, num=N*D).reshape(N, D)\n",
    "m = np.linspace(0.6, 0.9, num=N*D).reshape(N, D)\n",
    "v = np.linspace(0.7, 0.5, num=N*D).reshape(N, D)\n",
    "\n",
    "config = {'learning_rate': 1e-2, 'm': m, 'v': v, 't': 5}\n",
    "next_w, _ = adam(w, dw, config=config)\n",
    "\n",
    "expected_next_w = np.asarray([\n",
    "  [-0.40094747, -0.34836187, -0.29577703, -0.24319299, -0.19060977],\n",
    "  [-0.1380274,  -0.08544591, -0.03286534,  0.01971428,  0.0722929],\n",
    "  [ 0.1248705,   0.17744702,  0.23002243,  0.28259667,  0.33516969],\n",
    "  [ 0.38774145,  0.44031188,  0.49288093,  0.54544852,  0.59801459]])\n",
    "expected_v = np.asarray([\n",
    "  [ 0.69966,     0.68908382,  0.67851319,  0.66794809,  0.65738853,],\n",
    "  [ 0.64683452,  0.63628604,  0.6257431,   0.61520571,  0.60467385,],\n",
    "  [ 0.59414753,  0.58362676,  0.57311152,  0.56260183,  0.55209767,],\n",
    "  [ 0.54159906,  0.53110598,  0.52061845,  0.51013645,  0.49966,   ]])\n",
    "expected_m = np.asarray([\n",
    "  [ 0.48,        0.49947368,  0.51894737,  0.53842105,  0.55789474],\n",
    "  [ 0.57736842,  0.59684211,  0.61631579,  0.63578947,  0.65526316],\n",
    "  [ 0.67473684,  0.69421053,  0.71368421,  0.73315789,  0.75263158],\n",
    "  [ 0.77210526,  0.79157895,  0.81105263,  0.83052632,  0.85      ]])\n",
    "\n",
    "print 'next_w error: ', rel_error(expected_next_w, next_w)\n",
    "print 'v error: ', rel_error(expected_v, config['v'])\n",
    "print 'm error: ', rel_error(expected_m, config['m'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Once you have debugged your RMSProp and Adam implementations, run the following to train a pair of deep networks using these new update rules:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "running with  adam\n",
      "(Iteration 1 / 200) loss: 19.060417\n",
      "(Epoch 0 / 5) train acc: 0.122000; val_acc: 0.136000\n",
      "(Iteration 11 / 200) loss: 5.184338\n",
      "(Iteration 21 / 200) loss: 2.434020\n",
      "(Iteration 31 / 200) loss: 2.470745\n",
      "(Epoch 1 / 5) train acc: 0.175000; val_acc: 0.123000\n",
      "(Iteration 41 / 200) loss: 2.290166\n",
      "(Iteration 51 / 200) loss: 2.188520\n",
      "(Iteration 61 / 200) loss: 2.145554\n",
      "(Iteration 71 / 200) loss: 2.109677\n",
      "(Epoch 2 / 5) train acc: 0.254000; val_acc: 0.215000\n",
      "(Iteration 81 / 200) loss: 2.035233\n",
      "(Iteration 91 / 200) loss: 2.095865\n",
      "(Iteration 101 / 200) loss: 2.260805\n",
      "(Iteration 111 / 200) loss: 1.990908\n",
      "(Epoch 3 / 5) train acc: 0.252000; val_acc: 0.252000\n",
      "(Iteration 121 / 200) loss: 2.095937\n",
      "(Iteration 131 / 200) loss: 2.017085\n",
      "(Iteration 141 / 200) loss: 2.105191\n",
      "(Iteration 151 / 200) loss: 2.052403\n",
      "(Epoch 4 / 5) train acc: 0.266000; val_acc: 0.252000\n",
      "(Iteration 161 / 200) loss: 2.123900\n",
      "(Iteration 171 / 200) loss: 2.099334\n",
      "(Iteration 181 / 200) loss: 1.944074\n",
      "(Iteration 191 / 200) loss: 1.906906\n",
      "(Epoch 5 / 5) train acc: 0.294000; val_acc: 0.264000\n",
      "\n",
      "running with  rmsprop\n",
      "(Iteration 1 / 200) loss: 17.626202\n",
      "(Epoch 0 / 5) train acc: 0.135000; val_acc: 0.135000\n",
      "(Iteration 11 / 200) loss: 5.205939\n",
      "(Iteration 21 / 200) loss: 3.008412\n",
      "(Iteration 31 / 200) loss: 3.314699\n",
      "(Epoch 1 / 5) train acc: 0.282000; val_acc: 0.208000\n",
      "(Iteration 41 / 200) loss: 2.890181\n",
      "(Iteration 51 / 200) loss: 2.462674\n",
      "(Iteration 61 / 200) loss: 2.141356\n",
      "(Iteration 71 / 200) loss: 2.269344\n",
      "(Epoch 2 / 5) train acc: 0.318000; val_acc: 0.214000\n",
      "(Iteration 81 / 200) loss: 2.243077\n",
      "(Iteration 91 / 200) loss: 2.716616\n",
      "(Iteration 101 / 200) loss: 2.123442\n",
      "(Iteration 111 / 200) loss: 2.123675\n",
      "(Epoch 3 / 5) train acc: 0.433000; val_acc: 0.213000\n",
      "(Iteration 121 / 200) loss: 2.150039\n",
      "(Iteration 131 / 200) loss: 1.969909\n",
      "(Iteration 141 / 200) loss: 2.026227\n",
      "(Iteration 151 / 200) loss: 1.708779\n",
      "(Epoch 4 / 5) train acc: 0.477000; val_acc: 0.222000\n",
      "(Iteration 161 / 200) loss: 1.725335\n",
      "(Iteration 171 / 200) loss: 1.595996\n",
      "(Iteration 181 / 200) loss: 1.441710\n",
      "(Iteration 191 / 200) loss: 1.549240\n",
      "(Epoch 5 / 5) train acc: 0.566000; val_acc: 0.223000\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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//v2D+0N/7vubx+OhyO2OvF286m7+/W7L+fWGDRvG/PnzmTt3Lv379+eiiy7iq6++YsuW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aUlkyKjhMKdiIj0WVMTvPZabHbu1KlYmLvmGneLlKRYXV3i2bbo45MnYcqU5OFt2jS3MqP0q5Dj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\nyADCHQAAAABkAOEOAAAAADLg/wFziEbcBrrbWAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f425257e410>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "learning_rates = {'rmsprop': 1e-4, 'adam': 1e-3}\n",
    "for update_rule in ['adam', 'rmsprop']:\n",
    "  print 'running with ', update_rule\n",
    "  model = FullyConnectedNet([100, 100, 100, 100, 100], weight_scale=5e-2)\n",
    "\n",
    "  solver = Solver(model, small_data,\n",
    "                  num_epochs=5, batch_size=100,\n",
    "                  update_rule=update_rule,\n",
    "                  optim_config={\n",
    "                    'learning_rate': learning_rates[update_rule]\n",
    "                  },\n",
    "                  verbose=True)\n",
    "  solvers[update_rule] = solver\n",
    "  solver.train()\n",
    "  print\n",
    "\n",
    "plt.subplot(3, 1, 1)\n",
    "plt.title('Training loss')\n",
    "plt.xlabel('Iteration')\n",
    "\n",
    "plt.subplot(3, 1, 2)\n",
    "plt.title('Training accuracy')\n",
    "plt.xlabel('Epoch')\n",
    "\n",
    "plt.subplot(3, 1, 3)\n",
    "plt.title('Validation accuracy')\n",
    "plt.xlabel('Epoch')\n",
    "\n",
    "for update_rule, solver in solvers.iteritems():\n",
    "  plt.subplot(3, 1, 1)\n",
    "  plt.plot(solver.loss_history, 'o', label=update_rule)\n",
    "  \n",
    "  plt.subplot(3, 1, 2)\n",
    "  plt.plot(solver.train_acc_history, '-o', label=update_rule)\n",
    "\n",
    "  plt.subplot(3, 1, 3)\n",
    "  plt.plot(solver.val_acc_history, '-o', label=update_rule)\n",
    "  \n",
    "for i in [1, 2, 3]:\n",
    "  plt.subplot(3, 1, i)\n",
    "  plt.legend(loc='upper center', ncol=4)\n",
    "plt.gcf().set_size_inches(15, 15)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Train a good model!\n",
    "Train the best fully-connected model that you can on CIFAR-10, storing your best model in the `best_model` variable. We require you to get at least 50% accuracy on the validation set using a fully-connected net.\n",
    "\n",
    "If you are careful it should be possible to get accuracies above 55%, but we don't require it for this part and won't assign extra credit for doing so. Later in the assignment we will ask you to train the best convolutional network that you can on CIFAR-10, and we would prefer that you spend your effort working on convolutional nets rather than fully-connected nets.\n",
    "\n",
    "You might find it useful to complete the `BatchNormalization.ipynb` and `Dropout.ipynb` notebooks before completing this part, since those techniques can help you train powerful models."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "learning_rate = 0.001000, reg = 0.010000, best val loss = 0.475000\n",
      "learning_rate = 0.001000, reg = 0.001000, best val loss = 0.496000\n",
      "learning_rate = 0.001000, reg = 0.000100, best val loss = 0.508000\n",
      "learning_rate = 0.000100, reg = 0.010000, best val loss = 0.471000\n",
      "learning_rate = 0.000100, reg = 0.001000, best val loss = 0.427000\n",
      "learning_rate = 0.000100, reg = 0.000100, best val loss = 0.444000\n"
     ]
    }
   ],
   "source": [
    "best_model = None\n",
    "################################################################################\n",
    "# TODO: Train the best FullyConnectedNet that you can on CIFAR-10. You might   #\n",
    "# batch normalization and dropout useful. Store your best model in the         #\n",
    "# best_model variable.                                                         #\n",
    "################################################################################\n",
    "pass\n",
    "best_acc = 0\n",
    "for learning_rate in [1e-3, 1e-4]:\n",
    "    for reg in [1e-2, 1e-3, 1e-4]:\n",
    "        model = FullyConnectedNet([100, 100, 100, 100, 100], weight_scale=5e-2, reg = reg)\n",
    "\n",
    "        solver = Solver(model, data,\n",
    "                      num_epochs=10, batch_size=200,\n",
    "                      update_rule='rmsprop',\n",
    "                      optim_config={\n",
    "                        'learning_rate': learning_rate\n",
    "                      },\n",
    "                      verbose=False)\n",
    "        solver.train()\n",
    "        print 'learning_rate = %f, reg = %f, best val loss = %f' %(learning_rate, reg, solver.best_val_acc)\n",
    "        if solver.best_val_acc > best_acc:\n",
    "            best_acc = solver.best_val_acc\n",
    "            best_model = model\n",
    "################################################################################\n",
    "#                              END OF YOUR CODE                                #\n",
    "################################################################################"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Test you model\n",
    "Run your best model on the validation and test sets. You should achieve above 50% accuracy on the validation set."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Validation set accuracy:  0.508\n",
      "Test set accuracy:  0.476\n"
     ]
    }
   ],
   "source": [
    "y_test_pred = np.argmax(best_model.loss(data['X_test']), axis=1)\n",
    "y_val_pred = np.argmax(best_model.loss(data['X_val']), axis=1)\n",
    "print 'Validation set accuracy: ', (y_val_pred == data['y_val']).mean()\n",
    "print 'Test set accuracy: ', (y_test_pred == data['y_test']).mean()"
   ]
  }
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